JP2015079383A - Speech generation device, method, and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To stably generate a natural speech candidate for a user's speech.SOLUTION: An input part 4 accepts a speech intention and a word showing a topic of dialogue. An extraction part 60 extracts respective predicate term structures including case elements corresponding to words showing topics of dialogue from a predicate argument structure database 5 on the basis of the accepted word showing the topic of dialogue. An additional information imparting part 62 imparts additional information about expressions included in the predicate term structures with respect to the respective extracted predicate argument structures. A restriction determination part 64 extracts respective predicate argument structures that satisfy a restriction condition with respect to the speech intention accepted by the input part 4 on the basis of the restriction condition from the respective extracted predicate argument structures. With respect to the respective predicate argument structures extracted by the restriction determination part 64, a sentence generation part 65 generates a sentence following the speech intention accepted by the input part 4 and outputs, as a speech candidate, the generated sentence with respect to the predicate argument structure.

Description

  The present invention relates to an utterance generation device, method, and program.

  There are roughly two types of dialogue systems: task-oriented dialogue systems and non-task-oriented dialogue systems. The former achieves a specific task by interaction with the system, and is used, for example, in a flight reservation system or a weather information retrieval system. Since these systems can assume what is spoken in advance, the system maintains a database of utterances created manually, or applies weather information extracted from the database to manual templates, and the system utters utterances. (Non-Patent Document 1).

  A non-task-oriented dialogue system handles a dialogue with no purpose, and the content of the dialogue is a so-called chat. Since various topics are spoken in the chat, it is impossible to assume the content that is spoken in advance. Therefore, utterance generation is a very difficult task. In order to deal with a wide range of user input, recent conventional techniques include a database of texts such as the web and Twitter (registered trademark) and a system utterance selected by selecting something similar to the user utterance. (Non-patent document 2).

Ryuichiro Higashinaka, Katsuhito Sudoh, Mikio Nakano, `` Incorporating Discourse Features into Confidence Scoring of Intention Recognition Results in Spoken Dialogue Systems '', Speech Communication, 2006, Volume 48, Issues 3-4, p.417-436 Shibata, M., Nishiguchi, T., and Tomiura, Y, "Dialog system for open-ended conversation using web documents.", Infomatica, (2009), 33 (3), p.277-284

  However, if the utterance is generated by selecting the utterance from the web or Twitter (registered trademark) in the chat conversation, there is a possibility that the utterance according to the utterance intention of the system cannot be performed. For example, in a situation where the system determines that a user should ask a question about a certain topic, a question cannot be made unless there is a question sentence on the topic on the Web or Twitter (registered trademark). Since the system needs to ask questions and ask questions according to the situation, the inability to generate utterances according to the situation lowers the quality of the dialogue.

  The present invention has been made in view of the above circumstances, and an object thereof is to provide an utterance generation apparatus, method, and program capable of generating natural utterance candidates for a user's utterance.

  In order to achieve the above object, an utterance generation device according to the present invention is based on an input unit that receives an intention of an utterance and a word indicating a topic of dialogue, and a word indicating the topic of dialogue received by the input unit. A predescript word including the case element corresponding to the word indicating the topic of dialogue from a database storing a plurality of predicate term structures that are combinations of case elements corresponding to the predicate and the case element corresponding to the predescription word An extraction unit for extracting each of the term structures, an additional information adding unit for adding additional information related to an expression included in the previous description word term structure, for each of the previous description term term structures extracted by the extraction unit, and the extraction From each of the preceding description term structure extracted by the part, based on a predetermined restriction condition regarding the additional information and the intention of the utterance, the utterance of the utterance accepted by the input part A constraint determination unit that extracts each of the preceding description term structure satisfying the constraint condition with respect to the diagram, and each of the previous description word term structure extracted by the constraint determination unit, using the previous description word term structure, A sentence generation unit that generates a sentence according to the intention of the utterance accepted by the input unit and outputs the sentence generated for the previous description term structure as an utterance candidate.

  An utterance generation method according to the present invention is an utterance generation method in an utterance generation apparatus including an input unit, an extraction unit, an additional information adding unit, a constraint determination unit, and a sentence generation unit, and the input unit A case element that is a case element corresponding to a predicate and a predescription word based on the word indicating the topic of conversation received by the input unit by the extracting unit; From the database storing a plurality of predicate term structures that are combinations of the above, the step of extracting each predescription term term structure including the case element corresponding to the word indicating the conversation topic, and the additional information adding unit, For each of the preceding description term structures extracted by the extraction unit, a step of adding additional information related to an expression included in the preceding description term structure, and the constraint determination unit , For each utterance intention received by the input unit, based on a predetermined restriction condition regarding the additional information and the intention of the utterance, from each of the preceding description term structure extracted by the extraction unit Extracting each of the preceding description term structure satisfying the constraint condition, and using the preceding description term structure for each of the preceding description term structure extracted by the constraint determination unit by the sentence generation unit, Generating a sentence according to the intention of the utterance received by the input unit, and outputting the sentence generated for the previous description term structure as an utterance candidate.

  The sentence generation unit, for each of the previous description term structure extracted by the constraint determination unit, according to a predetermined order for the previous description word, the case element, and the case of the case element, Is generated in advance and is converted to a sentence end expression representing the intention of the utterance and the intention of the utterance received by the input unit. Based on the sentence end expression conversion rule, the sentence end expression of the plain text generated for the previous description term structure is converted to generate a sentence according to the intention of the utterance, and the previous description term structure generated A sentence can be output as an utterance candidate.

  Further, the extraction unit extracts and extracts each predescription term structure including case elements corresponding to the word indicating the topic of conversation from the database based on the word indicating the topic of conversation. Priorities according to the appearance frequency of the previous description term structure in the text data can be given to each of the previous description term structure.

  In addition, the extraction unit extracts, from the database, each predescription term structure including a case element corresponding to the word indicating the conversation topic from the database based on the word indicating the topic of the conversation and the extraction. In each of the predescriptor term structure, the priority is given to the predescription term structure consisting of predicate and C case element according to the appearance frequency of the predescription term structure in the text data. For the set of previous description term structure obtained from the extracted previous description term structure, the presence or absence of a predicate or case element is specified for each node in order from the root node representing the entire set of previous description term structure. Among the above conditions when the set of preceding description term structure represented by the node is divided, the division is performed according to the condition that minimizes the expected entropy of the set of preceding description term structure. The tree structure is constructed, and for each node of the tree structure, the predescription term structure obtained when tracing from the node to the root node corresponds to the position of the node in the tree structure. It is possible to give priority.

  In addition, the sentence generation unit generates the plaintext for each of the previous description word term structures extracted by the constraint determination unit, and the plaintext generated for the previous description word term structure and the previous description word term structure. For each of the pairs, the similarity between the context information calculated based on the input utterance history and the plaintext of the pair, and at least the priority assigned to the previous descriptive term structure of the pair Assigning a score based on one, transforming the sentence end expression of the plain text of the pair for each of the top M pairs of the given score, generating a sentence according to the intention of the utterance, The sentence generated for the pair can be output as an utterance candidate.

  Further, the additional information giving unit includes, as the additional information, information indicating the type of evaluation expression related to the predicate included in the previous description term structure, for each of the previous description terms structure extracted by the extraction unit, At least one of information indicating the type of evaluation expression relating to the noun and information indicating whether or not the question is included in the previous description word structure may be given.

  In addition, the constraint determination unit may be configured to add information indicating that the additional information is a question from each of the previous description term structure extracted by the extraction unit when the intention of the utterance is not a question. Not using a description term structure, if the intention of the utterance is a positive evaluation, using a previous description term structure to which information indicating a positive evaluation is given as the additional information, and When the intention of the utterance is a negative evaluation, the extraction unit uses, as the constraint condition, at least one of using a predescription term structure to which information indicating a negative evaluation is added as the additional information Each of the previous description term structure that satisfies the constraint condition with respect to the intention of the utterance received by the input unit is extracted from each of the previous description term structure extracted by It is possible to so that.

  The program according to the present invention is a program for causing a computer to function as each unit of the utterance generation device according to the present invention.

  As described above, according to the utterance generation device, method, and program of the present invention, each predicate term structure including a case element corresponding to a word indicating a topic of dialogue is stored in a database storing a plurality of predicate term structures. For each extracted predicate term structure, additional information related to the expression included in the predicate term structure is added, and the intention of the utterance is determined based on the predetermined constraints regarding the additional information and the intention of the utterance. On the other hand, each predicate term structure that satisfies the constraint condition is extracted, and for each extracted predicate term structure, a sentence according to the intention of the utterance is generated using the predicate term structure, and the generated sentence is By outputting as an utterance candidate, an effect that a natural utterance candidate for a user utterance can be generated is obtained.

It is a block diagram which shows one structural example of the predicate term structure database construction apparatus which concerns on embodiment of this invention. It is explanatory drawing which shows an example of Entity data. It is explanatory drawing which shows an example of Attribute data. It is explanatory drawing which shows an example of Event data. It is a block diagram which shows one structural example of the speech production | generation apparatus which concerns on embodiment of this invention. It is explanatory drawing which shows an example of an Entity frame. It is a figure which shows an example of the tree structure produced | generated about focus "mystill". It is a figure which shows an example of the tree structure produced | generated about focus "ramen". It is a figure which shows an example of an Attribute frame. It is a figure which shows an example of an Event frame. It is explanatory drawing which shows an example of the Entity frame to which the additional information was given. It is explanatory drawing which shows an example of the Attribute frame to which additional information was given. It is explanatory drawing which shows an example of the Event frame to which additional information was given. It is a flowchart which shows the content of the predicate term structure database construction process routine which concerns on embodiment of this invention. It is a flowchart which shows the content of the speech production | generation process routine which concerns on embodiment of this invention.

<Overview>
First, an outline of an embodiment of the present invention will be described.

  In the embodiment of the present invention, first, predicate term structure analysis is performed on large-scale text data to create a predicate term structure database. Each record of the predicate term structure database holds a predicate and its case element (predicate term structure) that are statements elements. The dialogue system extracts a record including a word indicating the topic of the current dialogue (hereinafter referred to as a focus) from the predicate term structure database and follows the intention of the dialogue system from the predicate term structure of the record. The utterance is generated by generating an utterance sentence and making it as an utterance candidate.

  An example in which the present invention is applied to an utterance generation apparatus configured with the utterance generation unit of the chat dialogue system as an apparatus will be described below.

  Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.

<System configuration of predicate term structure database construction device>
FIG. 1 is a block diagram showing a predicate term structure database construction device 100 according to an embodiment of the present invention. The predicate term structure database construction device 100 is composed of a computer including a CPU, a RAM, and a ROM storing a program for executing a predicate term structure database construction processing routine to be described later. It is configured as shown.

  As shown in FIG. 1, the predicate term structure database construction apparatus 100 according to the present embodiment includes a text data input unit 1, a predicate term structure database construction unit 2, and a predicate term structure database 3.

  The text data input unit 1 accepts input of a text set including a large amount of text data. The text data included in the text set may be any text data.

  The predicate term structure database construction unit 2 extracts a predicate term structure from the text data for each of a plurality of text data included in the text set received by the text data input unit 1 and stores it in the predicate term structure database 3. The predicate term structure database construction unit 2 includes a text database 20, a morpheme analysis unit 22, a dependency analysis unit 24, an initial data generation unit 26, an entity data generation unit 28, an Attribute data generation unit 30, And an event data generation unit 32.

  The text database 20 stores a text set received by the text data input unit 1.

  The morpheme analysis unit 22 performs morpheme analysis on each sentence of the plurality of text data included in the text set stored in the text database 20.

  The dependency analysis unit 24 performs dependency analysis on each sentence of the plurality of text data included in the text set based on the morpheme analysis result analyzed by the morpheme analysis unit 22 to identify the phrase and Determine the dependency structure.

  In addition, what is used free may be used for the morphological analysis by the morphological analysis part 22 and the dependency analysis by the dependency analysis part 24. For example, ChaSen or CaboCha. In this embodiment, JTAG and JDEP developed by the applicant are used for morphological analysis and dependency analysis, respectively.

  The initial data generation unit 26, for each sentence of the plurality of text data included in the text set, based on the dependency structure determined by the dependency analysis unit 24, and a case element corresponding to the predicate Is identified and extracted as a predicate term structure. The predicate term structure is a combination of a predicate and a case element corresponding to the predicate. Then, the initial data generation unit 26 temporarily stores the predicate term structure extracted for each sentence of the plurality of text data as initial data in a memory (not shown).

  In the present embodiment, the initial data generation unit 26, for each predicate in the sentence, as a case element of the predicate, C, G, W, D, De, G, C, and Each made case is extracted, and a combination of a predicate and one or more case elements is extracted as a group. Then, the initial data generation unit 26 collects the same predicate term structure extracted and registers the initial data together with the frequency in a memory (not shown).

  The initial data generation unit 26 will be described with a specific example. For example, if the sentence is “Taro meets Hanako”, “Meet” is a predicate, and from the clauses related to the clause of the predicate, the case element of the ga case is “Taro”, the case element of the two cases Is "Hanako". Then, the predicate term structure “predicate: meet ga case: Taro ni case: hanako” is extracted from here. Such a predicate term structure is extracted from all sentences of text data included in the text set.

  The entity data generation unit 28 extracts, from the initial data generated by the initial data generation unit 26, a pair of a predicate term structure composed of a case element and a predicate and a frequency of the predicate structure. Entity data composed of each pair of data is generated. Then, the Entity data generation unit 28 stores the generated Entity data in the predicate term structure database 3.

  For example, Entity data is composed of data of a pair of predicate term structure and frequency corresponding to sentences such as “Kyoto is hot” and “Mystil is good”. Further, FIG. 2 shows an example of Entity data excerpt relating to the focus “mystil”. The last column of the entity data in FIG. 2 is the frequency of the predicate term structure.

  The Attribute data generation unit 30 determines, from the initial data generated by the initial data generation unit 26, a pair of a predicate term structure including a predicate, a C case, and a case element of the case and a frequency of the predicate term structure. Extraction is performed, and Attribute data composed of the extracted data of each pair is generated. Then, the Attribute data generation unit 30 stores the generated Attribute data in the predicate term structure database 3.

  For example, Attribute data is composed of predicate term structure and frequency pair data corresponding to sentences such as “Kyoto is cold in winter” and “Mystil is good”. FIG. 3 shows an example of Attribute data.

The event data generation unit 32 uses at least one of a predicate, a case, a case, a case, a case, a case, a case, a case, and a case from the initial data generated by the initial data generation unit 26. A pair of a predicate term structure including one case element and a frequency of the predicate term structure is extracted, and event data composed of the extracted data of each pair is generated. Then, the event data generation unit 32 stores the generated event data in the predicate term structure database 3.
FIG. 4 shows an example of Event data. Note that FIG. 4 is an example of Event data when the display of C is omitted.

  The predicate term structure database 3 stores Entity data generated by the Entity data generation unit 28, Attribute data generated by the Attribute data generation unit 30, and Event data generated by the Event data generation unit 32. . That is, the predicate term structure database 3 stores a plurality of predicate term structures. The predicate term structure database 3 is configured as an RDB (Relational database) so as to be able to search for Entity data, Attribute data, and Event data.

<System configuration of utterance generation device>
FIG. 5 is a block diagram showing the utterance generation device 200 according to the embodiment of the present invention. This utterance generation device 200 is composed of a computer including a CPU, a RAM, and a ROM storing a program for executing an utterance generation processing routine described later, and is functionally configured as follows. Yes.

  As shown in FIG. 5, the utterance generation device 200 according to the present embodiment includes an input unit 4, a predicate term structure database 5, a calculation unit 6, and an output unit 7. The predicate term structure database 5 is an example of a database.

  The input unit 4 receives the intention of speech, the focus, and the speech history. The utterance history is a set of dialogue systems and user utterance character strings up to the present time.

  Further, in the present embodiment, there are four types of utterance intentions: “question”, “positive evaluation”, “negative evaluation”, and “other”. The intentions of the above utterances are the intention to ask the user a question, the intention to express a positive opinion about the focus (e.g. I love XX), and the intention to express a negative opinion about the focus, respectively. (Example: I don't like XX) and express other general intentions. The intention of the utterance is not limited to the above, and the kind of intention of the utterance may be added depending on the design of the dialogue system. For example, references (Toyomi Meguro, Ryuichiro Higashinaka, Koji Dosaka, Yasuhiro Minami, “Analysis of Listening Conversation and Construction of Dialogue Control Unit Based on Analysis”, IPSJ Journal, 2012, Vol. A set of dialogue action tags as described in Table 6 in pp. 2787-2801) may be used as the intention of the utterance.

  The predicate term structure database 5 stores the same entity data, attribute data, and event data as the entity data, attribute data, and event data stored in the predicate term structure database 3 of the above-described predicate term structure database construction device 100. Yes.

  The computing unit 6 includes an extracting unit 60, an additional information adding unit 62, a constraint determining unit 64, and a sentence generating unit 65. The sentence generation unit 65 includes a plain text generation unit 66, a scoring unit 68, and a sentence end expression conversion unit 70.

  Based on the focus received by the input unit 4, the extraction unit 60 uses the entity data, attribute data, and event data stored in the predicate term structure database 5 to generate a predicate term structure including a case element that matches the focus. Each of the extracted predicate term structures is extracted and given a priority according to the appearance frequency of the predicate term structure in the text data, and an Entity frame, an Attribute frame, and an Event frame are created. Each frame is a set of predicate term structure and priority pairs.

  The Entity frame, Attribute frame, and Event frame are created as described below.

  First, the extraction unit 60 extracts each predicate term structure including case elements matching the focus from the entity data stored in the predicate term structure database 5 based on the focus received by the input unit 4. For each of the predicate term structures, a priority according to the appearance frequency of the predicate term structure in the text data is given, and an Entity frame is created. Here, the extraction unit 60 acquires each of the predicate term structures having the top N appearance frequencies, and creates an Entity frame. For example, N is 500. FIG. 6 is an example of an Entity frame for the focus “Mystil”.

  In the Entity frame shown in FIG. 6, numbers 0 and 1 are assigned to predicates and case elements. This is a simple notation of the predicate term structure used in this embodiment. The predicates are number 0, C, G, W, D, De, To, Kara, and Made. Represented by numbers 1 to 7, respectively. Since the C case is often used as the subject in the same way as the ga case, the number 1 is assigned as in the case of the ga case. In the data in the Entity frame of FIG. 6, “0_good” indicates that the predicate is “good”, and “1_mystil” indicates that the case is “mistil”. As can be seen from the example of FIG. 6 above, the Entity frame includes a predicate term structure that represents an attribute of the Entity itself as a focus. Here, the priority indicates that a smaller number is prioritized. The priority is set so that priority is given in the order of appearance frequency.

  Next, based on the focus received by the input unit 4, the extraction unit 60 extracts each predicate term structure including case elements that match the focus from the Attribute data stored in the predicate term structure database 5. Based on the extracted set of predicate term structures, a tree structure representing the set of predicate term structures is constructed. Then, for each node of the constructed tree structure, the extraction unit 60 gives a priority according to the position of the node in the tree structure to the predicate term structure obtained when tracing from the node to the root node. Then, an Attribute frame is created. The extraction unit 60 extracts a predicate term structure including a focus on the case element of the attribute data from the Attribute data from the Attribute data.

  The tree structure that represents the set of predicate term structures is the condition when the set of predicate term structures represented by the nodes is divided for each node in order from the root node that represents the entire set of predicate term structures. Of these, it is constructed by dividing under a condition that the expected value of entropy of the set of divided predicate term structures is minimum.

  Hereinafter, a procedure for constructing a tree structure representing the set of predicate term structures will be described.

  In the present embodiment, an algorithm for learning a decision tree called ID3 is used for a tree structuring procedure of a set of predicate term structures. This is because when the set of predicate term structures is divided into two parts on the condition of the presence of a predicate or a case element (in the case of creating an Attribute frame, the presence of a predicate or a case element) The division is repeated under the condition that the expected value is the lowest (condition in which the information gain is large).

  A specific procedure is realized by the following processes (1) to (4).

(1) All predicates P (Predicates) and case elements A (Argents) are extracted from the extracted predicate term structure set PA.

(2) For x∈ (P∪A), PA is divided into two subsets depending on the presence or absence of x. The two divided subsets are set as YES _x and NO _x, and entropy H (YES _x ) and H (NO _x ) are calculated for each subset according to the following equations (1) and (2).

Here, p (y) is the appearance probability of y in YES _x . This can be easily calculated from the appearance frequency in the text data.

And according to the following formula (4), the entropy of the entire PA, that is,

If the expected value of entropy in the case of division is subtracted from the information gain, an information gain is obtained.

Here, p (YES _x ) and p (NO _x ) are the appearance probability of the predicate term structure including x and the appearance probability of the predicate term structure not including x in PA.

(3) x which maximizes IG (x) is obtained, and the processing of (2) to (3) is recursively repeated with YES _x and NO _x as PA ′.

(4) When the processes (2) to (3) are repeated until it cannot be divided, the predicate term structure set PA is converted into a tree structure depending on the presence or absence of elements that can be arranged most at each time point. .

  For example, in the case of the focus “mystil”, dividing the set of predicate term structures depending on whether the predicate has “good” has the largest information gain. Therefore, it is first divided into two subsets on the condition that “good is included in the predicate” or “good is not included in the predicate”. Thereafter, the division of the set of predicate term structures is repeated on the condition of “hobby” or “famous music”. FIG. 7 is an example of a tree structure created for the focal point “Mystil”. FIG. 8 is an example of a tree structure created for the focal point “ramen”.

  The elements included in the condition close to the root can be considered to be a large amount of information useful for organizing the entire predicate term structure, so the predicate term structures are arranged in order of the distance to the root. Then, what is given priority to the arranged predicate term structures is referred to as an Attribute frame. Specifically, when tracing from a certain node to the root, there is a predicate (a node meaning that there is a predicate P) on the path, and a case element (a node meaning that there is a certain case element A) If there is one or more, they are collected as a predicate term structure, and the depth of the node is set as the priority of the predicate term structure.

  Alternatively, the tree structure may be traced in order with the leftmost priority, and the order of the nodes that have been encountered may be set as the priority. In the example of FIG. 8 above, “0_divided 1_preference”, “0_parting 1_preference”, “0_wowed 1_preference”, “0_life 1_soup”, “0_cooking 1_soup”, “0_dense 1_taste” The priority is assigned from 1 in this order. In the present embodiment, the tree structure is derived from the ID3 by the leftmost priority recursive process. Therefore, according to this priority setting method, the priority is higher from the first processed, that is, the important one. Be treated. Also, nodes that are close and similar in content can have close priority.

  An example of an Attribute frame obtained by the present process from the tree structure for the focal point “mystil” is as shown in FIG. Here, depth is a priority. The depth from the root of “0_Good 1_Lyrics” is actually 2, but the minimum value of the predicate term structure depth obtained by this method is 2, so the priority is 1 from the depth. It has been drawn.

  The Attribute frame is data representing information related to a focus attribute (Attribute). “Lyric is good” is the predicate term with the highest priority, but the predicate term is the one with the predicate term closest to the root, and “whether the lyrics are good” or “not so” This represents a predicate term having a large amount of information that can largely divide the entire predicate term structure of the mystyl.

  As for the creation of the Attribute frame, as with the Entity frame, each of the top N occurrences of predicate term structures (for example, N is 500) in the text data is acquired and appears in the N predicate term structures. Attribute frames may be created by assigning priorities in order of frequency.

  Next, based on the focus received by the input unit 4, the extraction unit 60 extracts each predicate term structure including a case element that matches the focus from the event data stored in the predicate term structure database 5. Similar to the creation of the Attribute frame, a tree structure representing a set of predicate term structures is constructed for the set of extracted predicate term structures. Then, the extraction unit 60 assigns a priority according to the position of the predicate term structure in the tree structure to each predicate term structure included in the set of predicate term structures, and creates an Event frame. The extraction unit 60 extracts a predicate term structure in which the focus is included in any case element of the event data from the case case to the made case from the Event data.

  FIG. 10 is an example of an Event frame for the focal point “Mystil”. The Event frame is data representing a general situation related to the focus (Event). FIG. 10 is an example of an event frame when the display of the case including the focus is omitted.

  As for the creation of the event frame, each of the top N occurrences (for example, N is 500) of the predicate term structures in the text data is acquired, and the occurrence of the event frame appears in the N predicate term structures. An event frame may be created by assigning priorities in order of frequency.

  Note that Attribute data and Event data are an example of a set of predicate term structures different from the predicate term structure composed of predicates and C case elements. Further, each predicate term structure included in the Entity frame, the Attribute frame, and the Event frame is an example of each predicate term structure including a case element that matches the focus.

The additional information adding unit 62 adds additional information related to the expression included in the predicate term structure to each of the predicate term structures included in the Entity frame, Attribute frame, and Event frame created by the extraction unit 60.
The additional information includes information indicating the type of evaluation expression relating to the predicate included in the predicate term structure, information indicating the type of evaluation expression relating to the noun, and information indicating whether or not it is a question. It is a kind. Hereinafter, three types of additional information will be described.

(1) Information (PN) indicating the type of evaluation expression related to the predicate
The information indicating the type of evaluation expression related to the predicate includes the evaluation expression represented by the predicate such as “good” and “bad” in the predicate term structure, and if it is included, what type It is information that. P for positive polarity evaluation expressions, N for negative polarity evaluation expressions, PN for any expression, O (Other) for any other expression. As a list of evaluation expressions used here, a word list prepared in advance is used. Any method can be used to determine whether it is included as long as it can match words. For example, regular expression matching may be used.

(2) Information indicating the type of evaluation expression related to nouns (PNE)
As information indicating the type of evaluation expression related to a noun, P includes a noun evaluation expression (for example, “gold”, “love”, “happiness”, etc.) that is preferably increased as it increases. N is given if an evaluation expression of a noun that is not good (for example, illness, misfortune, etc.) is included, E is given if both can be taken, and O is given otherwise. A list prepared in advance is used as a list of these nouns.

(3) Information indicating whether it is a question (Q)
Any case element of the predicate term structure that begins with “who” or “what” is a question, so prepare word lists such as “who”, “what”, “when”, and “where” in advance. In addition, the case element of the predicate term structure is matched with the word in the word list, and as information indicating whether or not it is a question, Q for records having case elements including the word in the word list, and so on Otherwise, O is given.

  FIG. 11 is an example of an Entity frame that focuses on “accident”, to which the additional information (1) to (3) is added. Since the word “accident” is N as the type of evaluation expression regarding nouns, the column of PNE is N. “Scared” or the like is N because it is a negative polarity evaluation expression.

  FIG. 12 is an example of an Attribute frame that focuses on “accident”, to which the additional information (1) to (3) is added. The fifth “0_bad 1_who” contains the question word “who”, and is assigned Q. Note that FIG. 12 is an example of an Attribute frame when the display of the case including the focus is omitted.

  FIG. 13 is an example of an event frame focusing on “accident” to which the additional information (1) to (3) is added. Note that FIG. 13 is an example of an Event frame when the display of the case is omitted.

  The constraint determination unit 64 is based on a predetermined constraint condition regarding the additional information and the intention of the utterance from each of the predicate term structures included in the Entity frame, the Attribute frame, and the Event frame created by the extraction unit 60. Then, each predicate term structure that satisfies the constraint condition for the intention of the utterance accepted by the input unit 4 is extracted, and each extracted predicate term structure is output to the plaintext generation unit 66.

  Here, the predetermined constraint condition is that, when the intention of the utterance is not a question, the predicate term structure to which the information indicating the question is added is not used as additional information, and the intention of the utterance is positively evaluated. , Use predicate term structure with information indicating positive evaluation as additional information, and negative evaluation as additional information when intention of utterance is negative evaluation This is to use a predicate term structure to which information indicating is given.

  Specifically, when the intention of the utterance received by the input unit 4 is not the intention of the “question” utterance, the constraint determination unit 64 determines the Q of the frame in each of the predicate term structures included in each frame. Do not extract the predicate term structure with Q added to the column.

  In addition, when the intention of the utterance received by the input unit 4 is “an utterance relating to positive evaluation”, the constraint determination unit 64 includes the PN in the frame among the predicate term structures included in each frame. Alternatively, only the predicate term structure in which P is added to the PNE column is extracted.

  Further, when the intention of the utterance received by the input unit 4 is “an utterance regarding negative evaluation”, the constraint determination unit 64 includes the PN in the frame among the predicate term structures included in each frame. Or only the predicate term structure with N added to the PNE column.

  It can be ensured that utterances that cause inconsistencies in meaning due to the above restrictions are not made. For example, a plain text with a question word “who is bad”, an utterance with negative polarity as a positive evaluation “Is it OK to be sick”, and an utterance with a positive polarity as negative evaluation “be happy” It ’s not good ”.

  For each predicate term structure output by the constraint determination unit 64, the plaintext generation unit 66 follows the predetermined order, the predicate, the case element, and the case element in accordance with the order of the predicate term structure, And a plain text in which the case elements are arranged is generated.

  If the predicate of the predicate term structure is pred, the case is case, and the case element is arg, a prescriptive sentence “arg is pred” is generated if the predicate term structure is extracted from the Entity frame and the Attribute frame. For frames other than that, a plain text “arg case pred” is generated. For example, if the predicate term structure “0_good 1_Kyoto” extracted from the Entity frame is “Kyoto is good”. If the predicate term structure “0_sing 2_music” extracted from the event frame is “singing music”. In addition, from the Japanese characteristics, case elements are arranged so as to be closer to predicates in the order of wo case, ni case, ga case, de case, to case, made case, and empty case. Then, the plain text generation unit 66 outputs a list of pairs of the predicate term structure and the plain text generated for the predicate term structure to the scoring unit 68.

The scoring unit 68 is calculated based on the utterance history received by the input unit 4 for each pair of the predicate term structure output by the plaintext generation unit 66 and the plaintext generated for the predicate term structure. A score based on the similarity between the context information and the plaintext of the pair, and the priority assigned to the predicate term structure of the pair.
Specifically, the scoring unit 68 calculates a score according to the following formula for each pair of predicate term structure and plain text.

  In the above equation (5), the predicate term structure is F, and S is a plain text corresponding to F. C represents an utterance history, and L is a fixed value that defines how many previous utterances are referred to in the utterance history. L is, for example, 3. The dist function is a function that returns a meaningful collection of utterance history (context) and plain text.

  In the dist function, calculation using a concept base is performed. Specifically, first, the latest L utterance is extracted from the utterance history. Then, the concept base is referenced for each of the L utterances, and L concept vectors (1000 dimensions) are created.

  Here, a concept vector is created from the utterance as follows. First, utterances are morphologically analyzed and divided into words. Then, the concept base is searched for each word, and the sum of concept vectors corresponding to the found word is calculated as a result of the search. Then, the concept vector is normalized so that the sum of the concept vectors becomes 1, and is used as the concept vector of the utterance.

  For the plain text S, one concept vector is created in the same manner as the utterance concept vector.

  Then, a vector serving as the center of gravity is obtained from L + 1 vectors obtained by combining each concept vector of L utterances and the concept vector of the plain text S. The vector serving as the center of gravity is called a centroid.

  Finally, for each of L + 1 vectors, an error (square of Euclidean distance) between the vector and the centroid is calculated, and the average value of the calculated errors is used as a return value of the dist function. A smaller return value indicates that a plurality of vectors are collected.

  By using the return value of the dist function, it is possible to confirm whether or not the plaintext S is semantically organized with the previous context (L utterance histories).

  For details on the concept base, refer to the references (Katsuhito Bessho, Toshiro Uchiyama, Atsushi Uchiyama, Ryoji Kataoka, Masahiro Oku, “Corpus concept base generation method based on co-occurrence between words and semantic attributes”, Transactions of Information Processing Society of Japan. 2008, Vol. 49, No. 12, pp. 3997-4006).

  Briefly describing the concept base, the concept base is a word represented by a vector composed of the appearance frequencies of neighboring words. Since the vector space is sparse in the distribution of the words themselves, the dimensional compression method is used to collect words having the same meaning and suppress the vectors to 1000 dimensions. By calculating the distance between words on a 1000-dimensional vector, it is possible to calculate a semantic distance between words.

  The priority function in the above equation (5) is a score based on the priority of each frame. α and β are coefficients, and are used to set which of the dist function and the priority function should be emphasized. Further, by setting either one to 0, it is possible to perform scoring using only one function. Scoring allows you to rank important things that are relevant to the context of the dialogue.

  Note that the top K items of the dist function may be acquired first, and the result sorted according to the priority function may be used as the ranking result. Alternatively, the top J items of the priority function may be acquired first, and the result sorted according to the dist function may be used as the ranking result.

  The scoring unit 68 sorts the pairs of predicate term structures and plain texts according to the score calculated according to the above equation (5), and outputs the top M sort results to the sentence end expression conversion unit 70.

  The sentence end expression conversion unit 70 outputs the score output by the scoring unit 68 based on the intention of the utterance received by the input unit 4 and the sentence end expression conversion rule for converting to the sentence end expression representing the intention of the utterance. For each of the top M pairs, the sentence end expression of the plain text of the pair is converted to generate a sentence according to the intention of the utterance, and the sentence generated for the pair is output as an utterance candidate.

  Specifically, the sentence end expression conversion unit 70 converts the sentence end expression of the pretext of the pair of the predicate term structure and the plain sentence according to the sentence end expression conversion rule, rewrites the sentence according to the intention of the utterance, To do. The sentence ending expression conversion rule is implemented by a rule prepared in advance. The sentence end expression conversion rule is a rewrite rule based on the part of speech of the last word of the plain text.

  For example, if the adjective verb is the last word, such as a plain text “I like mystil”, if the intention of the utterance is “question”, “do you like” like the sentence “do you like mystil?” To do. If the last word is a verb, such as a plain text “Mystil sings”, a “no?” Is added and a sentence “Does mystil sing?”. Or, by referring to the given dictionary and using the last verb as a continuous form (“singing”) and adding “do you want?” To generate the sentence “Would you like to sing?” . Further, when a plurality of sentences are generated for one plain text, any sentence is randomly selected as an utterance candidate.

  In the case of other utterance intentions, the plain text is used as an utterance candidate as it is, or a sentence end expression candidate according to the part of speech of the last word is prepared and the sentence end expression is given. For example, when the last part of speech is a noun, there are a plurality of sentence end expression candidates such as “dayo”, “dane”, and “dayone”, and any one of the plurality of sentence end expression candidates is assigned to the sentence end to make a speech candidate. Note that a more complicated rule corresponding to the intention of the utterance may be used as the sentence ending expression conversion rule.

  Then, the sentence ending expression conversion unit 70 outputs K utterance candidates obtained as a result of converting the sentence ending expression for each plain text of the top M pairs. Here, since a plain text or the like to which the sentence end expression conversion rule cannot be applied is not converted, K is a number of M or less.

  The output unit 7 outputs the K utterance candidates output by the sentence end expression conversion unit 70 as a result. The output from the output unit 7 becomes an utterance candidate in the dialogue system of the upper module, and finally any one is used as the system utterance.

<Operation of predicate term structure database construction device>
Next, the operation of the predicate term structure database construction device 100 according to the present embodiment will be described. First, when a text set including a plurality of text data is input to the predicate term structure database construction device 100, the predicate term structure database construction device 100 executes a predicate term structure database construction processing routine shown in FIG.

  First, in step S100, the text data input unit 1 accepts input of a text set and stores it in the text database 20.

  In step S102, the predicate term structure database construction unit 2 sets one text data among a plurality of text data included in the text set stored in the text database 20 in step S100.

  In step S104, one sentence is set out of the sentences included in the text data set in step S102.

  In step S106, the morpheme analysis unit 22 performs morpheme analysis on the sentence set in step S104.

  In step S108, the dependency analysis unit 24 performs dependency analysis on the sentence set in step S104 based on the morphological analysis result analyzed in step S106. Determine the dependency structure.

  In step S110, the initial data generation unit 26 corresponds to the predicate of the sentence set in step S104 and the predicate for the sentence set in step S104 based on the dependency structure determined in step S108. The case element that is the element of the case to be identified is identified and extracted as a predicate term structure. In step S110, the extracted predicate term structure is temporarily stored in a memory (not shown) as initial data.

  In step S112, it is determined whether or not the processing in steps S104 to S110 has been executed for all sentences included in the text data set in step S102. If there is a sentence that does not execute the processes in steps S104 to S110, the process returns to step S104. On the other hand, if the processes in steps S104 to S110 have been executed for all sentences included in the text data set in step S102, the process proceeds to step S114.

  In step S114, it is determined whether or not the processes in steps S102 to S112 have been executed for all text data stored in the text database 20. If there is text data that has not been subjected to the processes in steps S102 to S112, the process returns to step S102. On the other hand, when the processes in steps S102 to S112 are executed for all text data stored in the text database 20, the process proceeds to step S116.

  In step S116, the entity data generation unit 28 uses the initial data stored in the memory (not shown) in step S110 to calculate a predicate term structure including a case element and a predicate and a frequency of the predicate structure. Pairs are extracted, and entity data composed of the extracted data of each pair is generated. Then, the entity data is stored in the predicate term structure database 3 by the entity data generation unit 28.

  In step S118, the Attribute data generation unit 30 creates a predicate term structure consisting of a predicate, a case, and a case from the initial data stored in the memory (not shown) in step S110, and the predicate term structure. A pair with the frequency is extracted, and Attribute data composed of the extracted data of each pair is generated. Then, Attribute data is stored in the predicate term structure database 3 by the Attribute data generation unit 30.

  In step S120, from the initial data stored in the memory (not shown) in step S110 by the event data generation unit 32, a predicate, a case, a case, a case, a case, a case, a case, A pair of a predicate term structure including at least one of a color case and a made case and a frequency of the predicate term structure is extracted, and event data composed of the extracted data of each pair is generated. Then, the event data is stored in the predicate term structure database 3 by the event data generation unit 32, and the predicate term structure database construction processing routine is terminated.

<Operation of utterance generator>
Next, the operation of the utterance generation device 200 according to the present embodiment will be described. First, when Entity data, Attribute data, and Event data stored in the predicate term structure database 3 of the predicate term structure database construction device 100 are input to the utterance generation device 200, they are stored in the predicate term structure database 5. The When an utterance intention, focus, and utterance history are input to the utterance generation device 200 from a higher-level module such as a dialogue system, the utterance generation device 200 executes an utterance generation processing routine shown in FIG. .

  First, in step S200, the input unit 4 accepts an utterance intention, focus, and utterance history.

  In step S202, based on the focus received in step S200, the extraction unit 60 extracts each predicate term structure including case elements that match the focus from the entity data stored in the predicate term structure database 5. A priority according to the appearance frequency of the predicate term structure in the text data is assigned to each extracted predicate term structure, and an Entity frame is created.

  In step S204, the extraction unit 60 extracts each predicate term structure including case elements matching the focus from the Attribute data stored in the predicate term structure database 5, based on the focus received in step S200. Based on the extracted set of predicate term structures, a tree structure representing the set of predicate term structures is constructed. Then, with respect to each node of the constructed tree structure by the extraction unit 60, a priority according to the position of the node in the tree structure is given to the predicate term structure obtained corresponding to the node, and Event Create a frame.

  In step S206, based on the focus received in step S200, the extraction unit 60 extracts each predicate term structure including case elements that match the focus from the event data stored in the predicate term structure database 5. As in step S204, a tree structure representing a set of predicate term structures is constructed based on the set of extracted predicate term structures. Then, with respect to each node of the constructed tree structure by the extraction unit 60, a priority according to the position of the node in the tree structure is given to the predicate term structure obtained corresponding to the node, and Event Create a frame.

  In step S208, each of the predicate term structure included in the Entity frame created in step S202, the Attribute frame created in step S204, and the Event frame created in step S206 by the additional information adding unit 62. , Additional information regarding expressions included in the predicate term structure is given.

  In step S210, the constraint determination unit 64 uses the Entity frame created in step S202, the Attribute frame created in step S204, and the predicate term structure included in the event frame created in step S206. Each predicate term structure that satisfies the constraint condition with respect to the intention of the utterance accepted in step S200 is extracted, and each extracted predicate term structure is output.

  In step S212, the prescription generation unit 66 arranges the predicates, case elements, and case elements of the predicate term structure in accordance with a predetermined order for each of the predicate term structures output in step S210. Generate a plain text.

  In step S214, the utterance history received in step S200 and the predicate for each pair of the predicate term structure generated in step S212 and the plaintext generated for the predicate term structure by the scoring unit 68 Based on the priority given to the term structure, the score is calculated and given according to the above equation (5).

  In step S216, the sentence ending expression conversion unit 70 assigns the utterance intention accepted in step S200 and the sentence ending expression conversion rule for converting into the sentence ending expression expressing the intention of the utterance in step S214. In addition, for each of the top M pairs of scores, the sentence end expression of the plain text of the pair is converted to generate K sentences according to the intention of the utterance, and the K sentences generated for the pair are Output as utterance candidate.

  In step S218, the output unit 7 outputs the K utterance candidates output by the sentence end expression conversion unit 70 as a result, and ends the utterance generation processing routine.

  Note that the utterance candidate output in step S218 is used for utterance by a higher-level module such as a dialogue system. In the upper module, for example, if there are a plurality of utterance candidates, one of them is uttered at random.

  As described above, according to the utterance generation device according to the present embodiment, each predicate term structure including case elements matching the focus is extracted from the predicate term structure database storing a plurality of predicate term structures. For each of the predicate term structures, the predicate term structure that gives the additional information related to the expression included in the predicate term structure and satisfies the constraint conditions based on the predetermined constraint conditions regarding the additional information and the intention of the utterance For each of the extracted predicate term structures, a sentence according to the intention of the utterance is generated using each of the extracted predicate term structures, and the generated sentence is output as an utterance candidate. Natural utterance candidates for can be generated.

  Further, when generating utterance candidates, the quality of the utterance candidates can be improved by preferentially using predicate term structures that appear frequently in the text data.

  In addition, by constructing a tree structure that represents a set of predicate term structures according to information gain rather than the frequency of appearance of the predicate term structure in text data, it is used in preference to the predicate term structure containing the most important case elements. , Utterance candidates can be generated.

  In addition, it is possible to generate an utterance candidate more suited to the context by selecting a plain text in consideration of the semantic similarity between the utterance history and the plain text generated from the predicate term structure.

  Furthermore, by considering the polarity of words included in the predicate term structure and the presence or absence of interrogative words, it is possible to generate utterance candidates more in line with the utterance intention of the dialogue system.

  In addition, the dialogue system can generate utterance candidates in accordance with the situation on the current topic. Thereby, the dialogue between the user and the dialogue system becomes smooth, and the user and the dialogue system can easily cooperate.

  Note that the present invention is not limited to the above-described embodiment, and various modifications and applications are possible without departing from the gist of the present invention.

  For example, in the above embodiment, the case where the predicate term structure database construction device 100 and the utterance generation device 200 are configured as separate devices has been described as an example. However, the predicate term structure database construction device 100 and the utterance generation device 200 are You may comprise as one apparatus.

  In addition, the scoring unit 68 in the utterance generation device 200 of the above embodiment, for each pair of the predicate term structure and the plaintext generated for the predicate term structure, the context information and the plaintext of the pair The case where the score based on the similarity and the priority given to the predicate term structure of the pair is given has been described as an example, but the present invention is not limited to this, and the similarity between the context information and the plaintext of the pair You may give the score based on either one of the degree and the priority given to the predicate term structure of the said pair.

  Further, in the utterance generation device 200, the scoring unit 68 sorts the pairs of predicate term structures and plain texts according to the score calculated according to the above equation (5), and converts the top M items of the sorting results to the sentence end expression conversion unit. The case of outputting to 70 has been described as an example, but the present invention is not limited to this, and all the pairs of the predicate term structure and the plaintext are output to the sentence end expression conversion unit 70 without calculating the score. The sentence end expression of the plain text may be converted to generate a sentence according to the intention of the utterance, and the generated sentence may be output as the utterance candidate.

  In addition, as an example, a case where a predicate item structure including at least one of predicate, C case, ga case, wo case, d case, de case, to case, color case, and made case is extracted as event data is taken as an example. Although described, the present invention is not limited to this. For example, as the event data, a predicate term structure including a predicate and at least one of a case, a case, a case, a case, a case, a case, and a case may be extracted.

  Moreover, although the above-mentioned predicate term structure database construction apparatus 100 demonstrated the case where the text database 20 and the predicate term structure database 3 were provided, for example, at least 1 of the text database 20 and the predicate term structure database 3 is a predicate term. The predicate term structure database construction device 100 provided in the external device of the structure database construction device 100 refers to at least one of the text database 20 and the predicate term structure database 3 by communicating with the external device using communication means. You may make it do.

  Moreover, although the above-mentioned utterance production | generation apparatus 200 demonstrated the case where the predicate term structure database 5 was provided, for example, the predicate term structure database 5 is provided in the external device of the utterance production | generation apparatus 200, and the utterance production | generation apparatus 200 is external. The predicate term structure database 5 may be referred to by communicating with the apparatus using the communication means.

  The predicate term structure database construction device 100 and the utterance generation device 200 described above have a computer system inside. If the “computer system” uses a WWW system, a homepage providing environment ( Or a display environment).

  In the present specification, the embodiment has been described in which the program is installed in advance. However, the program can be provided by being stored in a computer-readable recording medium.

DESCRIPTION OF SYMBOLS 1 Text data input part 2 Predicate term structure database construction part 3, 5 Predicate term structure database 4 Input part 6 Operation part 7 Output part 20 Text database 22 Morphological analysis part 24 Dependency analysis part 26 Initial data generation part 28 Entity data generation part 30 Attribute data generation unit 32 Event data generation unit 60 Extraction unit 62 Additional information addition unit 64 Constraint determination unit 65 Sentence generation unit 66 Plain text generation unit 68 Scoring unit 70 Sentence expression conversion unit 100 Predicate term structure database construction device 200 Utterance generation apparatus

Claims (9)

An input unit that accepts an intention of utterance and a word indicating a topic of dialogue;
Based on a word indicating the topic of the dialogue received by the input unit, from a database storing a plurality of predicate term structures that are combinations of a predicate and a case element that is a case element corresponding to the previous descriptive word, An extractor for extracting each of the preceding description term structure including the case element corresponding to a word indicating a topic;
For each of the previous description term structure extracted by the extraction unit, an additional information giving unit for giving additional information related to the expression included in the previous description term term structure;
From each of the preceding description term structure extracted by the extraction unit, the intention of the utterance received by the input unit based on a predetermined restriction condition regarding the additional information and the intention of the utterance A constraint determination unit that extracts each of the preceding description term structure satisfying the constraint condition;
For each of the previous description term structure extracted by the constraint determination unit, a sentence according to the intention of the utterance accepted by the input unit is generated using the previous description term structure, and the previous description term structure is generated. A sentence generator that outputs the sentence as an utterance candidate;
An utterance generating device including The sentence generation unit, for each of the previous description term structure extracted by the constraint determination unit, according to a predetermined order for the previous description word, the case element, and the case of the case element, Is generated in advance and is converted to a sentence end expression representing the intention of the utterance and the intention of the utterance received by the input unit. Based on the sentence end expression conversion rule, the sentence end expression of the plain text generated for the previous description term structure is converted to generate a sentence according to the intention of the utterance, and the previous description term structure generated The utterance generation device according to claim 1, wherein the sentence is output as an utterance candidate.   The extraction unit extracts each predescription term structure including case elements corresponding to the word indicating the topic of conversation from the database based on the word indicating the topic of conversation, and extracts the previous description The utterance generation device according to claim 2, wherein each word term structure is given a priority according to the appearance frequency of the previous description word term structure in the text data.   The extraction unit extracts, from the database, each predescription term structure including a case element corresponding to the word indicating the topic of conversation, based on the word indicating the topic of conversation, and the extracted For each of the previous description term structure consisting of a predicate and a case element of each of the previous description term structure, a priority is given according to the appearance frequency of the previous description term structure in the text data, With respect to a set of previous description term structure obtained from the extracted previous description term structure, for each node in order from the root node representing the entire set of previous description term structure, the presence or absence of a predicate or case element is used as a condition. Of the above conditions when the set of predescription term structure represented by the node is divided, it is constructed by dividing under the condition that the expected value of entropy of the divided set of predescription term structure is minimum. Priorities according to the position of the node in the tree structure with respect to the predescription term structure obtained when the tree structure is constructed and each node of the tree structure is traced from the node to the root node The utterance generation device according to claim 2, wherein The sentence generator
For each of the previous description term structure extracted by the constraint determination unit, the plain text is generated, and for each of the pair of the previous description term structure and the previous description term structure generated for the previous description term structure, Giving a score based on at least one of the similarity between the context information calculated based on the input utterance history and the plaintext of the pair, and the priority assigned to the previous descriptive term structure of the pair;
For each of the top M pairs of the given scores, the sentence end expression of the plain text of the pair is converted to generate a sentence according to the intention of the utterance, and the sentence generated for the pair is uttered The utterance generation device according to claim 3, which is output as a candidate. The additional information adding unit includes, as the additional information, information indicating the type of evaluation expression relating to the predicate included in the previous description term structure, and the previous description for each of the previous description term structure extracted by the extraction unit The utterance generation according to any one of claims 1 to 5, wherein at least one of information indicating a type of evaluation expression related to a noun and information indicating whether or not the question is included in the word structure is given. apparatus. The restriction determination unit includes a predescription word to which information indicating a question is added as the additional information when the intention of the utterance is not a question, from each of the predescription term structure extracted by the extraction unit When no intention structure is used, when the intention of the utterance is positive evaluation, a predescription term structure to which information indicating positive evaluation is added as the additional information is used, and the utterance When the intention is a negative evaluation, the extraction unit extracts at least one of using the predescription term structure to which information indicating negative evaluation is added as the additional information as the constraint condition Each of the predescript term structure that satisfies the constraint condition with respect to the intention of the utterance received by the input unit The utterance generation device according to any one of claims 1 to 6. An utterance generation method in an utterance generation device including an input unit, an extraction unit, an additional information addition unit, a constraint determination unit, and a sentence generation unit,
Receiving the intention of the utterance and the word indicating the topic of dialogue by the input unit;
A database that stores a plurality of predicate term structures that are combinations of predicates and case elements that are case elements corresponding to previous descriptive words, based on words indicating the topic of conversation received by the input unit by the extracting unit. Extracting each of the predescription term structure including the case element corresponding to the word indicating the topic of the dialogue;
A step of adding additional information related to an expression included in the previous description term term structure to each of the previous description term term structures extracted by the extraction unit by the additional information giving unit;
The utterance received by the input unit based on a predetermined restriction condition regarding the additional information and the intention of the utterance from each of the preceding description term structures extracted by the extraction unit by the constraint determination unit Extracting each predescription term structure that satisfies the constraint condition for the intention of
For each of the previous description term structure extracted by the constraint determination unit, the sentence generation unit generates a sentence according to the intention of the utterance accepted by the input unit using the previous description term structure. Outputting the sentence generated for the description term structure as an utterance candidate;
Utterance generation method including   The program for functioning a computer as each part of the speech production | generation apparatus of any one of Claims 1-7.