KR102033327B1 - Apparatus and method for building sorting corpus by user participation - Google Patents

Apparatus and method for building sorting corpus by user participation Download PDF

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KR102033327B1
KR102033327B1 KR1020150084039A KR20150084039A KR102033327B1 KR 102033327 B1 KR102033327 B1 KR 102033327B1 KR 1020150084039 A KR1020150084039 A KR 1020150084039A KR 20150084039 A KR20150084039 A KR 20150084039A KR 102033327 B1 KR102033327 B1 KR 102033327B1
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sorting
user
sentence
word
module
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KR20160147375A (en
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김창현
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한국전자통신연구원
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Abstract

The sorting module receives the type of the game and the purpose of the game, and presents the sorting method according to the type of the game and the purpose of the game to the user, the sentence sorting step of receiving a sentence or word alignment, the sorting module A user evaluation step of determining a level of a user by monitoring a progress of sorting, a sentence recommendation module evaluating a sorted sentence using a level of the user, and a sentence recommendation module evaluating the sorted sentence And a sentence recommendation step of recommending a sentence that improves performance of statistical machine translation (SMT) using the result of the user and the level of the user.

Description

PARAMETER AND METHOD FOR BUILDING SORTING CORPUS BY USER PARTICIPATION

The present invention relates to a user-participating sorting corpus building apparatus and method for building a sorting corpus that is a source of various knowledge extraction for automatic translation.

At present, the biggest flow of automatic translation technology is statistical-based automatic machine translation (Statistical Machine Translation) method. Statistical-based automatic translation is a method of performing word-by-word sorting by applying a machine learning method based on a statistical model to a corpus arranged in sentence units. . Therefore, for statistical-based automatic translation, an ordered corpus is required and wording mapping information can be correctly extracted from the ordered corpus.

However, there are not many language pairs that exist on a scale level where sentence-aligned corpus is required, which is a deadly obstacle especially in the development of multilingual automatic translation technology. In such a situation, constructing a sentence-aligned corpus through an individual language expert requires a lot of time and money, and it is difficult to construct a corpus aligned by words.

In the case of sentence-based corpus, statistics-based automatic translation automatically obtains word-by-word sorting results using the automatic sorting methodology, but the automatic sorting results include a large number of errors, resulting in a decrease in translation performance. There are two main types of errors in this automatic sorting result.

First, errors occur due to problems with the automatic alignment methodology itself. In other words, it is not easy to generate a perfect sort result using the automatic sort method.

The second is an error due to inadequate sentences. In other words, due to sentences that are not easy to sort automatically, sorting errors occur, which degrades the performance of statistics-based automatic translation. For this reason, in case of statistics-based automatic translation, performance is not improved because there are many parallel corpus unconditionally, and it is possible to improve the performance by selecting the appropriate sentences for sorting.

Therefore, there is a need for a method of selecting a sentence that can improve the statistics-based automatic translation performance by constructing a correct word-based corpus.

Korean Laid-Open Patent Publication 2012-0018675 (2012.03.05.)

An object of the present invention is to solve the above problems, and based on a user-participated environment in a game form, the user can easily construct a sentence or word unit corpus through a game to improve statistics-based automatic translation performance. This is to help you select a sentence that is helpful.

Furthermore, an object of the present invention is to provide a user with low language ability according to the user's purpose and to improve the foreign language ability, and to store the user level to evaluate the user's quality.

In order to achieve the above object, a user-participating sorting corpus building method according to an embodiment of the present invention, the sorting module receives the type of the game and the purpose of the game, the sorting method according to the type of game and the purpose of the game to the user Presenting a sentence sorting step to receive a sentence or word alignment, the sorting module monitors the user's sorting progress to determine the user's level, the sentence recommendation module using the user's level, Sentence evaluation step to evaluate the sorted sentences and sentence recommendation step that the sentence recommendation module recommends sentences that improve the performance of statistical machine translation (SMT) by using the result of the sorted sentence evaluation and the user level It includes.

In the sentence sorting step, the sorting module receives a selection of a game type and a purpose of the game, the sorting module presents a sorting method to the user, and uses the same to sort the sentence or word of the user, and the sorting module is an example. Searching for and providing example sentences to the user.

In the step of arranging a sentence or word, the sorting module generates a word unit by chunking a plurality of words until the sorting is completed.

In providing an example sentence, the sorting module may provide a word unit dictionary, a phrase unit dictionary, and a corpus example sentence to the user.

The user evaluation step is characterized by evaluating the user by comparing the difficulty of the sentence, the time spent on the sentence alignment, and the sentence alignment result with other users' alignment results or existing alignment information.

In order to achieve the above object, a user participatory sorting corpus building apparatus according to an embodiment of the present invention receives a type of a game and a purpose of a game, and presents a sorting method according to the type of a game and a game to a user. A sorting module that receives the sorting of sentences or words, monitors the user's sorting progress to determine the level of the user, and evaluates the sorted sentences using the user's level. It includes a sentence recommendation module for recommending a sentence that improves the performance of statistical machine translation (SMT) using a database and a database for storing the user's level and sorted sentences.

The sorting module retrieves an example sentence while receiving an alignment of a sentence or a word and provides an example sentence to the user.

The sorting module may generate a word unit by chunking a plurality of words until the sorting is completed.

The example sentence is characterized by being a word unit dictionary, a phrase unit dictionary, and a corpus example sentence.

The sentence recommendation module is characterized by evaluating the user by comparing the difficulty of the sentence, the time spent in the sentence alignment, and the sentence alignment result with other users' alignment results or existing alignment information.

According to the present invention, by using a user-participatory environment in the form of a game, users with various levels of language abilities can easily build a sentence or word unit corpus through a game in large quantities, thereby increasing the accuracy of the alignment.

In addition, according to the present invention, it is possible to select only sentences that contribute to the improvement of statistics-based automatic translation performance by evaluating user level, thereby improving the performance of statistics-based automatic translation.

Furthermore, according to the present invention, a user who has a low level of language can improve his or her language ability, and can obtain information of users having a high level of language by making a database of user level evaluations.

1 is a view for explaining the configuration of the user participatory alignment corpus building apparatus according to an embodiment of the present invention.
2 is a flowchart illustrating a user participatory sorting corpus building method according to an embodiment of the present invention.
3 is a flowchart illustrating a sentence alignment step of aligning a sentence or a word according to an exemplary embodiment of the present invention.
4 is a flowchart illustrating an alignment method according to an exemplary embodiment of the present invention and a user's alignment using the alignment method.
5 is a view for explaining a step-by-word alignment method according to an embodiment of the present invention.

Hereinafter, the preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings so that those skilled in the art may easily implement the technical idea of the present invention. . First of all, in adding reference numerals to the components of each drawing, it should be noted that the same reference numerals are used as much as possible even if displayed on different drawings. In addition, in describing the present invention, when it is determined that the detailed description of the related well-known configuration or function may obscure the gist of the present invention, the detailed description thereof will be omitted.

Hereinafter, a user participatory alignment corpus building apparatus and method according to an embodiment of the present invention will be described in detail with reference to the accompanying drawings.

1 is a view for explaining the configuration of a user participatory alignment corpus building apparatus. 2 is a flowchart illustrating a user participatory sorting corpus building method. 3 is a flowchart illustrating a sentence sorting step of sorting a sentence or a word. 4 is a flowchart illustrating a method of arranging a user using the method of presenting the sorting method. FIG. 5 is a diagram for describing a word-by-word alignment method. FIG.

As shown in FIG. 1, the user participatory sorting corpus constructing apparatus includes an alignment module 100, a controller 200, a database 300, and a sentence recommendation module 400.

At this time, the sorting module 100 provides the user with various types of sentence or word sorting games and receives an input for selecting a sentence or word sorting game from the user. Then, a sentence or word unit game is used to receive an input for the alignment of the sentence or word unit of the user. At this time, the sorting module 100 searches for examples that are suitable for the user to perform sorting from a resource capable of knowing examples of correct sentences, including a corpus and a dictionary, so that the user can proceed with the game smoothly. can do. Further, the sorting module 100 automatically evaluates the language ability of the user and determines the level of the user while the user sorts the sentence or word as the game progresses. Further, the user's language skills are used to readjust the user's level and determine the degree of benefit the user can receive. The alignment module 100 may identify an error with respect to a sentence or word arranged by the user and provide feedback to the user based on the error.

The controller 200 mediates the exchange of information between the sorting module 100, the database 300, and the sentence recommendation module 400. In more detail, the user's level and sorting result determined by the sorting module 100 are stored in the database 300, and the user's level and sorting result stored in the database 300 are provided to the sentence recommendation module 400.

Database 300 stores the user-level database for storing the level of each user who progressed the game through the sorting module 100 and the sorted sentences that are the result of the game proceeded through the sorting module 100, statistics-based automatic translation ( Includes a sentence database to improve the performance of Statistical Machine Translation (SMT).

The sentence recommendation module 400 evaluates the sentences sorted by the user based on the user's level and the sorting result stored in the database 300, and sorts the sentences based on the evaluation of the sentences sorted by the user. The degree of contribution to the automatic translation performance is evaluated to determine whether the user-aligned sentences improve or decrease the performance of statistics-based automatic translation. In addition, the sentence recommendation module 400 recommends a sentence that can improve the performance of the statistics-based automatic translation using the results of the evaluation of the sentences stored by the user and the sentences of the statistics-based automatic translation previously stored.

As shown in FIG. 2, in the user-participating sorting corpus building method, a sentence sorting step (S100) of sorting a sentence or a word using a game selected by the user through the sorting module 100, and the user proceeds to sort the game. While the sorting module 100 monitors the user's sorting progress, the user evaluation step of determining a user's level by determining the language ability of the user (S200) and the sentence recommendation module 400 are the sorting module 100. Based on the level of the user judged by the sorting sentence evaluation step (S300) for evaluating the degree to which the alignment sentence contributes to the performance improvement of the statistics-based automatic translation and the pre-stored statistics-based automatic translation of the sentences aligned with the sentence Sentence recommendation step (S400) for recommending a sentence that improves the performance of statistics-based automatic translation using the evaluation results.

Here, in the sentence sorting step (S100) of sorting a sentence or a word, the user selects the type of game and the purpose of the game, and the sorting module 100 presents the sorting method to the user, and the user uses the sentence or word. Arranging and searching and providing example sentences suitable for the user to perform the sorting in order for the user to play the game smoothly.

In the user evaluation step (S200) in which the sorting module 100 determines the user's language ability to determine the user's level of alignment, in the sentence sorting step S100 of sorting a sentence or a word, the user does not correctly generate the entire sorting result. If not, the sorting module 100 determines why the user did not correctly produce the entire sorting result.

That is, the user did not produce the entire sorting result correctly because the sentence pair is not a correct translation, the sentence pair is a paraphrase sentence that is not easy to sort, or the user's level is insufficient to produce the correct sorting result. Can be. The alignment module 100 uses these reasons to determine the level of the user.

More specifically, while the user is playing the alignment game, the alignment module 100 monitors the alignment process of the user to determine the alignment level of the user and the accuracy of the alignment result. The user's level is used in the process of determining the user's game level in the future, and it can be used to determine the accuracy of the user's alignment result. At this time, the sorting module 100 may compare the difficulty of the sentence to be sorted, the time spent in sorting the sentence, and the sentence sorting result with other users' sorting results or existing sorting information in order to determine the user's language ability and sorting level. have.

At this time, the sorting module 100 determines the sorting level of the user, and can divide the level of the user into three levels of beginner, intermediate, and advanced. However, this level of alignment does not necessarily have to be divided into three stages, but can be divided into fewer stages or more stages. At this time, the difficulty of the sentence depends on the length of the sentence and the difficulty of the words used, the longer the length of the sentence, the higher the difficulty of the sentence, the more difficult the words used in the sentence, the higher the difficulty of the sentence. When judging the level of users using the speed of sorting sentences, the time spent sorting the sentence, the difficulty information of the sentence, and the time spent by the other users having different levels to sort the sentence You can judge. In addition, in order to determine the level of the user, it is possible to determine the level of the user by calculating the degree of coincidence using the result of sentence sorting using the sorting results of other users and the existing sorting information already held.

Furthermore, the level of the user can play the same role as the certification for the foreign language.

Furthermore, the user participatory sorting corpus building method according to an embodiment of the present invention, after the user evaluation step (S200) of determining the level of the user, the user feedback step of feeding back information about the error of the user's alignment result to the user It may further include. In this case, the user may receive feedback about the error and correct the alignment result of the sentence or may determine that the error feedback is wrong and ignore it.

The sorting module 100 performs a series of processes in which the user selects the type and purpose of the sorting game, sorts sentences, provides examples to the user, evaluates the level of the user, and provides feedback to the user. The collected information is transmitted to the controller 200, and the controller 200 provides the information to the database 300 and stores the collected information in a user level database and a sentence database, respectively.

In this case, in order to evaluate the sorting sentence (S300), the sentence recommendation module 400 compares the sentences sorted by the current user by comparing the sentences sorted by other users and pre-stored existing sorting results and the like. Evaluate the accuracy of

More specifically, a case in which the original sentence is not aligned with the unit of the translated sentence may occur in the aligned sentence. If there is a unit that has not yet been sorted even though the sentence has been sorted, the sentence recommendation module 400 determines the sorting result using the level of the user determined by the sorting module 100 and stored in the database 300. Evaluate.

For example, if the user's level is low, the sentence recommendation module 400 determines that the result is due to a low level of the user and excludes it from sentence selection when there is a unit that has not yet been sorted. On the other hand, even if the level of the user is high, even if the complete sentence is still in the unit that is not yet completed, it is determined that this is caused by the error of the sentence itself, not the problem of the user. Therefore, in this case, the sentence recommendation module 400 may determine that the unaligned units exist only in one language and do not exist in the opposite language. Furthermore, these units can be judged as causing errors in automatic sorting. In other words, the sorting result constructed by high-level users can be used to identify errors.

Furthermore, in the sentence recommendation step (S400) for recommending a sentence that improves the performance of statistics-based automatic translation, the sentence recommendation module 400 uses sentences and statistics-based sentences that improve the performance of the existing statistics-based automatic translation. It can be divided into sentences that degrade the performance of automatic translation. In other words, using the sentences evaluated in accordance with the user's level in the evaluation step (S300) by selecting the sentence to help improve the performance of statistics-based automatic translation, and to help improve the performance of statistics-based automatic translation You can exclude sentences that don't work.

For example, the sentence recommendation module 400 excludes sentences sorted by a low-level user and sentences that have been sorted within a short time, and recommends sentences in which the sorting result is matched between users or sentences sorted by a high-level user. have.

As shown in FIG. 3, in the step S100 of sorting a sentence or a word, a step in which a user selects a type of a game and a purpose of the game in step S110, and the sorting module 100 presents a sorting method to the user. The step S120 of arranging by using the user and the sorting module 100 include a step S130 of searching for and providing an example sentence suitable for performing the sorting by the user for a smooth game.

In more detail, in step S110, when the user selects the type of game and the purpose of the game, the user may select whether to execute a game in sentence unit alignment or a game in word unit alignment. Here, the text to be sorted by the user may be provided by the sorting module 100, and the user may directly select a sentence to be sorted.

In this case, when the user executes the sentence-based sorting game, the user plays a game based on the document unit that sorts the original document into the translated document. When the user executes the word-based sorting game, the user reads the original sentence. You will play a game based on sentence units sorted by translation sentences.

When the user selects the type of game to proceed, the sorting module 100 presents the screen of the type suitable for the type to the user. That is, when the user executes the sentence-based sorting game, the sorting module 100 provides the original document to be translated to the user. When the user executes the word-based sorting game, the sorting module 100 should translate to the user. Provide the original sentence to do. At this time, the user can select the purpose of the game to proceed. For example, when a user wants to learn a word, a sorting game for learning a word may be executed, and when the user wants to perform a sorting operation for constructing a sorting corpus, a sorting game for constructing a sorting corpus may be executed.

In this case, when the user executes a sorting game for learning a word, the sorting module 100 does not provide the first sorting result to the user in accordance with the purpose of the user.

On the other hand, when the user executes the alignment game for building the alignment corpus, the alignment module 100 may provide the first alignment result to the user in order to speed up the alignment operation.

Here, the primary alignment result may be a dictionary-based translation result and a machine translation result. In other words, when a user runs a sorting game for constructing a sorting corpus, the user does not need to directly translate and sort all the sentences that he or she is sorting. can do.

Thereafter, the sorting module 100 presents the sorting method to the user and in the step S120 of the sorting using the sorting method, when the user sets the sorting unit and the sorting purpose of the game, the sorting module 100 sets the sorting module 100. ) Takes input for the user's alignment. In this case, when the user completes the sorting, the user may repeat the process of checking and rearranging a new sentence or document to be sorted by inputting a 'next' command. In addition, the user may repeat the process of checking and reordering a new sentence or document to be sorted by inputting a 'pass' command if he or she cannot complete the sorting.

At this time, the user performs the sorting by using the sorting unit for each word unit, which will be described later, through the sorting module 100.

As the user progresses the sorting, the sorting module 100 searches for and provides an example sentence suitable for the user to perform the sorting in order to smoothly play the game (S130). The user may receive various information for the sorting game progressing. Can be. When the user selects a unit to sort and makes a request for information to the sorting module 100, the sorting module 100 provides the user with information and example sentences related to the unit.

For example, the sorting module 100 may provide a word and phrase unit dictionary to a user. That is, when the unit is in the dictionary, the information included in the dictionary may be provided to the user. Also, the alignment module 100 may provide a corpus example sentence to the user. That is, the sorting module 100 searches for and provides a user with example sentences matching or similar to the unit that the user wants to sort.

In this case, when the sorting module 100 provides a corpus example to the user, the sorting module 100 searches the vocabulary and meaning of individual words constituting the unit to the user in order to search for an example similar to the unit to be sorted. , Considering similar words, and opposite words, and provide the most useful example to the user. For example, in order to sort the 'number of articles', the sorting module 100 may identify that the 'items' and the 'items' are synonymous, and search for an example sentence of 'number of articles' to provide the user.

As shown in FIG. 4, when the user executes the word-based sorting game, the sorting module 100 presents the sorting method to the user and sorts the word-by-word to the user in step S120 of using the same. Provides a method of sorting by words. In other words, when the user starts sorting for the first time, the user tries to sort by one word (S121). If the alignment is successful by one word unit, the alignment is completed (S122). On the other hand, if a given sentence is a phrase that the user can not sort by one word unit, the sorting module 100 requests the user to sort by two words again (S121) and similarly, if the user succeeds in the two-word unit sorting If the step is completed (S122), and the phrase that the user can not sort in units of 2 words, the sorting module 100 requests the user to sort in units of 3 words (S121) and repeats the above-described steps until the user succeeds in sorting. .

Here, the sorting module 100 provides the user with a step-by-step sorting method for each word, so that the word-word, word-word, phrase-by-word unit is clearly distinguished so that the user can easily learn the word through the game. This is to ensure accurate alignment at the same time. In addition, it is possible to minimize the inaccuracy of the alignment by using the word-by-word alignment method.

Referring to FIG. 5, the sorting method according to each word unit will be described in more detail. The sorting module 100 lists all words that can be sorted in individual word units to the user. For example, if the user sorts the phrase 'number of articles', the sorting module 100 sorts the word 'items' in 'things' and 'number' in 'number' in units of one word. Subsequently, the sorting module 100 performs a chunking operation on the words that can be sorted in units of two words so that the number is bundled in one unit. However, since 'of' does not belong to any sorting yet, the sorting module 100 performs a chunking operation on words that can be sorted in units of three words. However, in the example illustrated in FIG. 5, there is no word that can be aligned in units of three words. Thus, the alignment module 100 again provides a four word alignment to the user. In this case, 'the number of items' are all grouped into one unit, and the phrase 'number of items' is sorted by 'the number of items'. In other words, the alignment is completed by sorting all the words as described above. In this way, when the user wants to learn the word, the sorting module 100 provides accurate word and phrase learning to the user, and when the user plays a sorting game for constructing the sorting corpus, the effect of obtaining the correct sorting result is obtained. Will be created.

By using the word-by-word sorting method as described above, the sorting module 100 may obtain a more accurate sorting result. For example, the sorting result of 'number' is 'number' in the sorting unit of 1 word, and the sorting result of 'number' is 'the number' in the sorting unit of 2 word, and the sorting module 100 sorts these two sorts. You can judge all the results as correct sorting results, not errors.

Although a preferred embodiment according to the present invention has been described above, it can be modified in various forms, and those skilled in the art can make various modifications and modifications without departing from the claims of the present invention. It is understood that it may be practiced.

100: alignment module 200: control unit
300: database 400: sentence recommendation module

Claims (10)

A sentence sorting step of providing a sentence or a word word sorting game to a user in a sorting module to receive a sentence or word alignment according to the progress of the sentence or word sorting game of the user;
A user evaluation step of determining a level of the user by monitoring the progress of the user's alignment in the alignment module;
An order sentence evaluation step of evaluating an ordered sentence using a level of the user in a sentence recommendation module; And
Constructing a user-participating sorting corpus comprising a sentence recommendation step of recommending a sentence that improves performance of statistical machine translation (SMT) using the result of the alignment sentence evaluation and the level of the user in the sentence recommendation module Way. The method of claim 1,
The sentence sorting step,
Receiving, by the sorting module, a selection about a type of game and a purpose of the game;
Presenting a sorting method to the user by the sorting module, arranging sentences or words of the user to correspond to the sorting method;
And the sorting module searches for an example sentence and provides the searched example sentence to the user. The method of claim 2,
Arranging the sentence or word,
And the sorting module generates a word unit by chunking a plurality of words until sorting is completed. The method of claim 3,
In providing the example sentence,
And the sorting module provides a word unit dictionary, a phrase unit dictionary, and a corpus example. The method of claim 1,
The user evaluation step,
A method for constructing a user-participating sorting corpus, which evaluates the user by comparing a sentence difficulty, time spent sorting a sentence, and a sentence sorting result with other sorting results or existing sorting information. A sorting module for providing a sentence or word sorting game to a user to monitor the sorting progress of the user according to the progress of the sentence or word sorting game of the user to determine the level of the user;
A sentence that evaluates the sentence sorted using the level of the user, and recommends a sentence that improves the performance of statistical machine translation (SMT) using the result of the sorted sentence evaluation and the level of the user. Recommended module and
And a database for storing the user's level and the sorted sentences. The method of claim 6,
The alignment module,
A user participatory sorting corpus constructing device for searching for example sentences while receiving the alignment of the sentence or word and providing the example sentences to the user. The method of claim 7, wherein
The alignment module
A user-participating sorting corpus building device that generates word units by chunking a plurality of words until sorting is complete. The method of claim 8,
The example sentence,
A user participatory sorting corpus building apparatus, characterized in that it is a word unit dictionary, a phrase unit dictionary, and a corpus example sentence. The method of claim 6,
The sentence recommendation module,
And a sentence difficulty level, time spent sorting sentences, and a sentence sorting result, which are compared with other users' sorting results or existing sorting information.
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