WO2020166125A1 - Système de génération de données de traduction - Google Patents

Système de génération de données de traduction Download PDF

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Publication number
WO2020166125A1
WO2020166125A1 PCT/JP2019/039337 JP2019039337W WO2020166125A1 WO 2020166125 A1 WO2020166125 A1 WO 2020166125A1 JP 2019039337 W JP2019039337 W JP 2019039337W WO 2020166125 A1 WO2020166125 A1 WO 2020166125A1
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noise
language text
source language
label
translation
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PCT/JP2019/039337
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English (en)
Japanese (ja)
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聡一朗 村上
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株式会社Ｎｔｔドコモ
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Priority to JP2020572078A priority Critical patent/JP7194759B2/ja
Publication of WO2020166125A1 publication Critical patent/WO2020166125A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning

Definitions

  • One aspect of the present invention relates to a translation data generation system.
  • the translation accuracy is reduced due to the fact that the user's natural utterance input includes stagnation, rewording, or fillers (hereinafter these may be collectively referred to as "noise"). There is a case.
  • Patent Document 1 and Patent Document 2 there is known a technique of identifying a reworded portion in an utterance and correcting the utterance content of the user.
  • One aspect of the present invention has been made in view of the above situation, and an object thereof is to perform highly accurate translation even for natural speech including noise.
  • a translation data generation system includes a noise adding unit that adds noise to a source language text to obtain a noise added source language text, a noise added source language text, and noise of the noise added source language text. And a corpus constructing unit for constructing a pseudo bilingual corpus in which a target language text corresponding to a source language text before being assigned is associated.
  • noise is added to the source language text, and the pseudo-translation corpus in which the noise-added source language text and the target language text corresponding to the source language text before the addition of noise are associated with each other. Is built.
  • the bilingual corpus in which the source language text with noise is associated with the target language text corresponding to the source language text before adding noise using such a bilingual corpus, for example, natural speech Even when noise such as filler is included in the input, it is possible to appropriately derive the target language text corresponding to the source language text before noise addition.
  • the translation data generation system of one aspect of the present invention it is possible to construct a robust corpus (pseudo bilingual corpus) for natural utterances including noise, and for natural utterances including noise. However, it is possible to translate with high accuracy.
  • the translation data generation system may further include a translation model learning unit that learns a translation model using a pseudo bilingual corpus. By learning the translation model based on the constructed corpus, it is possible to translate the natural utterance containing noise with higher accuracy.
  • the translation data generation system further includes a noise model learning unit that uses a training data that is a source language text group containing noise to learn a noise model related to the addition of noise to the source language text, and the noise addition unit is ,
  • a noise model may be used to add noise to the source language text.
  • a noise model is learned based on a source language text group that includes noise in advance, and noise is added based on the noise model, so that noise that is likely to be actually included is easily added. The accuracy can be further improved.
  • the noise adding unit adds a noise label indicating the type of noise to each word of the source language text, and replaces the noise label with the word corresponding to the noise label Noise may be added to the text. Since the noise label corresponding to each word of the source language text is added and then the word (noise) corresponding to the noise label is derived, it is possible to ensure the easiness and validity of the noise addition.
  • the noise adding unit may derive a plurality of patterns of words to be replaced with respect to one noise label, and obtain a plurality of patterns of noise adding source language text from one source language text. As a result, it is possible to efficiently enhance the pseudo bilingual corpus from one source language text and further improve the translation accuracy.
  • the noise adding unit may derive a plurality of patterns of noise labels corresponding to each word and obtain a plurality of patterns of noise-added source language text from one source language text. As a result, it is possible to efficiently enhance the pseudo bilingual corpus from one source language text and further improve the translation accuracy.
  • the noise adding unit may add a noise label according to the characteristics of each word in the source language text. This makes it possible to appropriately give each word a noise label relating to noise that is likely to be included in association with each word.
  • the noise adding unit may add a noise label according to at least one of the morpheme, the part of speech, and the reading of the word, which are the features of each word of the source language text. This makes it possible to appropriately give each word a noise label relating to noise that is likely to be included in association with each word.
  • the noise adding unit samples the noise label according to the probability distribution of each noise level based on the score of each noise label output from the noise model with the feature of each word of the source language text as an input, You may decide the noise label given to a source language text.
  • a noise label having a high score output from the noise model it is possible to give a noise label having a high score output from the noise model, and it is possible to appropriately give each word a noise label relating to noise that is likely to be included in association with each word.
  • the noise model may be constructed by a method using a conditional random field or neural network. Thereby, the noise model can be appropriately configured by machine learning.
  • FIG. 1 is a diagram schematically showing a processing image of the translation data generation system 1 according to the present embodiment.
  • the translation data generation system 1 adds noise to a text (source language text) on the source language side of an existing bilingual corpus (generally used bilingual corpus), and at the same time, adds noise to the source language text ( A source language text (noise-added) and a target language side text (target language text) corresponding to the source language text before noise are associated to construct a pseudo-translation corpus, and a machine translation model is created using the pseudo-translation corpus.
  • This is a system for learning (constructing) (for example, NMT (Neural Machine Translation) model).
  • the noise here is stagnation, rewording, filler, or the like that may be included in the user's natural speech input.
  • FIG. 2 is a diagram showing a functional configuration of the translation data generation system 1 according to the present embodiment.
  • the translation data generation system 1 includes a translation data generation device 10, a parallel translation corpus DB 20, a training information DB 30, a noise model learning device 40 (noise model learning unit), and a translation model learning.
  • the apparatus 50 (translation model learning unit).
  • the translation data generation system 1 does not necessarily have to have the above-described components, and may be configured with only the translation data generation device 10, for example, or the translation data generation device 10 and the noise model. It may be configured by only the learning device 40, or may be configured by only the translation data generation device 10, the noise model learning device 40, and the translation model learning device 50.
  • the parallel translation corpus DB 20 is a database that stores the parallel translation corpus.
  • a bilingual corpus is a structured combination of source language text and target language text.
  • the bilingual corpus stored in the bilingual corpus DB 20 may be a commonly used bilingual corpus, and is, for example, a Japanese/English bilingual corpus such as KFTT (Kyoto Free Translation Task) or BTEC.
  • the translation data generation device 10 adds noise to the source language text of the bilingual corpus stored in the bilingual corpus DB 20 to generate a pseudo bilingual corpus (details will be described later).
  • the training information DB 30 is a database that stores training information (training data) for learning a noise model (details will be described later).
  • the training information is a source language text group (transcription corpus of natural utterances, utterance data for learning) in which noise is annotated in advance.
  • Such training information is constructed, for example, by annotating a source language text included in a normal corpus with noise.
  • the noise model learning device 40 uses the training information (training data that is a source language text group including noise) stored in the training information DB 30 to learn a noise model related to adding noise to the source language text.
  • the learning data (training data) of the noise model for example, a transcription corpus of a Japanese utterance corpus (CSJ) or a spontaneous speech corpus such as Switch Board Corpus may be used.
  • the noise model outputs noise label information related to the source language text when the source language text is input.
  • the noise label is information indicating the type of noise.
  • FIG. 4 is a diagram illustrating a noise label. As shown in FIG. 4, in this embodiment, there are three types of noise labels, ⁇ F>, ⁇ D>, and 0.
  • ⁇ F> is a noise label indicating a filler.
  • ⁇ D> is a noise label indicating stagnation or rewording.
  • 0 is a noise label indicating that there is no noise.
  • the noise label information is information in which the type of noise label ( ⁇ F>, ⁇ D>, 0 described above) and a word (specifically, morpheme) associated with each noise label are associated with each other. Noise label sequence.
  • FIG. 3 is a diagram for explaining the outline of the noise model.
  • the noise model is constructed using, for example, a bi-directional recursive neural network (BiRNN) that is widely used in part-of-speech tagging and proper expression extraction tasks. ing.
  • the noise model may be constructed by a method using another neural network such as RNN or a method using a conditional random field such as CRF (Conditional random field).
  • the noise model has been trained to predict an appropriate noise label for each input element (word-specific morpheme) of the input source language text when noise is next to the element.
  • w (w 0 , w 1 ,..., W n ) of a source language text.
  • a noise label sequence l ( ⁇ F>,0,0, ⁇ D>, ⁇ F>,0,0) is generated based on the same learning utterance data in which noise is annotated. It Finally, BiRNN is learned as a sequence labeling problem that predicts the noise label sequence 1 from the morpheme sequence w. In BiRNN, parameter learning is performed using the prediction error of the output sequence with respect to the input sequence.
  • the translation model learning device 50 learns a translation model using the pseudo-parallel translation corpus constructed in the translation data generation device 10.
  • a Translation model a Transformer or RNN-based Sequence-to-Sequence model or the like may be used.
  • the translation data generation device 10 includes, as its functions, an analysis unit 11, a noise addition unit 12, a corpus construction unit 13, and a storage unit 14.
  • the noise adding unit 12 adds noise to the source language text (specifically, the morpheme sequence extracted by the analyzing unit 11) to obtain the noise adding source language text.
  • the noise imparting unit 12 imparts noise to the source language text using the noise model learned by the noise model learning device 40.
  • the noise imparting unit 12 imparts a noise label to each morpheme according to the characteristic (specifically, morpheme) of each word of the source language text, and assigns the noise label to the word corresponding to the noise label (the word as noise. ) To add noise to the source language text.
  • the noise adding unit 12 predicts a noise label sequence corresponding to the morpheme sequence of the input source language text by using the noise model, and inserts a noise label next to the corresponding morpheme sequence.
  • the noise adding unit 12 replaces the inserted noise label with a word representing noise, and obtains a noise-added source language text that is the final output and is a source language text with noise added.
  • the noise imparting unit 12 has been described as imparting a noise label according to the morpheme of the source language text, but the present invention is not limited to this, and the noise label is assigned according to the part of speech or reading (pronunciation) of each word in the source language text. May be given. Further, the noise adding unit 12 may add a noise label according to two or more pieces of information such as a morpheme of a word, a part of speech, and a reading.
  • the noise adding unit 12 first inputs the morpheme sequence of the source language text into the noise model, and acquires the output vector h t of the noise model at each time step (each morpheme sequence).
  • the noise label at each time step the one that has the maximum posterior probability of the noise label is not simply used as the estimation result, but a value exp(h t / ⁇ that is an exponent of the output vector h t ) Is determined by sampling based on the multinomial distribution defined in (1). That is, the noise label l t at each time step is estimated based on the following equation (1).
  • l t is the estimation result of the noise label
  • h t is the output vector of the noise model
  • ⁇ is the temperature parameter.
  • the output vector h t is represented by a three-dimensional vector for three label types ( ⁇ F>, ⁇ D>, 0).
  • the temperature parameter ⁇ is a parameter for operating the strength of variation of the noise label. When the value of the temperature parameter ⁇ is increased ( ⁇ ), the noise label probability distribution approaches a uniform distribution, and when the temperature parameter ⁇ is decreased ( ⁇ 0), the noise label with the highest probability is selected.
  • the determination of the noise label when the temperature parameter ⁇ is relatively small will be described.
  • h t / ⁇ ( ⁇ 0.6666..., 2, ⁇ 2).
  • exp(h t / ⁇ ) (0.51, 7.39, 0.13).
  • the probability distribution becomes (0.06 (probability that 0 is selected as a noise label), 0 .92 (the probability that ⁇ F> is selected as the noise label) and 0.02 (the probability that ⁇ D> is selected as the noise label)).
  • Sampling (trial) the noise label only once based on such a probability distribution corresponds to sampling from the categorical distribution. In this case, the establishment of the noise label ⁇ F> is extremely high at 92%, and the possibility of being selected as the sampling result is extremely high.
  • the determination of the noise label when the temperature parameter ⁇ is relatively large will be described.
  • the probability distribution is (0.30 (0 is the probability that 0 is selected as a noise label), 0 .45 (the probability that ⁇ F> is selected as a noise label) and 0.25 (the probability that ⁇ D> is selected as a noise label)).
  • the probability of establishment of each noise label approaches 33.333...%, and the probability distribution approaches a uniform distribution.
  • the noise adding unit 12 samples the noise label according to the probability distribution based on the score of each noise label output from the noise model, with the feature (morpheme sequence) of each word of the source language text as an input, and the source language text.
  • the noise label to be given to is determined.
  • the probability distribution defined based on the output value of the noise model is described as representing a polynomial distribution, but the present invention is not limited to this, and the probability distribution represents a Poisson distribution, a normal distribution, or the like. It may be.
  • the noise adding unit 12 then replaces the noise label sequence predicted using the noise model with a word representing noise.
  • the noise adding unit 12 performs sampling, for example, from the vocabulary set V type corresponding to each noise label based on the unigram probability. For example, when replacing the noise label ⁇ F> of the filler with the word representing the filler, the word representing the filler is determined based on the following equation (2).
  • w t ′ ⁇ V ⁇ F> ⁇ (2) In the above formula (2), V ⁇ F> is a vocabulary set of noise labels ⁇ F>, and w t ′ is a word representing a filler (noise) inserted at time step t.
  • the noise imparting unit 12 obtains a plurality of patterns of noise imparting source language text from one source language text.
  • the noise adding unit 12 may derive, for example, a plurality of patterns of words (words representing noise) to be replaced with respect to one noise label, and obtain a plurality of patterns of noise added source language text from one source language text. Further, the noise adding unit 12 may derive a plurality of patterns of noise labels corresponding to each morpheme, and obtain a plurality of patterns of noise-added source language text from one source language text.
  • the corpus construction unit 13 constructs a pseudo bilingual corpus in which the noise-added source language text and the target language text corresponding to the noise-added source language text before the noise addition are associated with each other.
  • FIG. 5 is a diagram showing an image of constructing a pseudo bilingual corpus.
  • the noise-added source language text “I want to drive the tourist route better than the main expressway”, which is the source language text before adding noise (“Early tourism than main expressways I want to run on the route”, etc.), and the target language text corresponding to the source language text before noise addition, “I would rather take a scenic route than a main highway.”
  • a pseudo-translation corpus (corresponding to a translation pair) associated with is constructed.
  • the storage unit 14 is a DB that stores the pseudo bilingual corpus constructed by the corpus construction unit 13.
  • the translation model learning device 50 learns a translation model using the pseudo-parallel translation corpus stored in the storage unit 14.
  • FIG. 6 is a flowchart showing the processing executed by the translation data generation system 1. It is assumed that the noise model is constructed (learned) by the noise model learning device 40 as a premise for executing the processing shown in FIG.
  • the analysis unit 11 of the translation data generation device 10 acquires the source language text from the parallel translation corpus DB 20 (step S1). Subsequently, the analysis unit 11 executes morphological analysis on the acquired source language text (step S2).
  • the noise adding unit 12 of the translation data generating device 10 adds noise to the morpheme sequence extracted by the analyzing unit 11 to obtain a noise-added source language text (step S3). Specifically, the noise adding unit 12 predicts a noise label sequence corresponding to the morpheme sequence of the input source language text by using the noise model, and inserts a noise label next to the corresponding morpheme sequence. Then, the noise adding unit 12 replaces the inserted noise label with a word representing noise, and obtains a noise-added source language text that is the final output and is a source language text with noise added.
  • the corpus construction unit 13 of the translation data generation device 10 associates the noise-added source language text with the target language text corresponding to the noise-added source language text before the noise addition.
  • a corpus is constructed (step S4).
  • the translation model learning device 50 learns a translation model using the pseudo bilingual corpus constructed by the corpus construction unit 13 (step S5).
  • the above is an example of the processing executed by the translation data generation system 1.
  • the translation data generation system 1 includes a noise adding unit 12 that adds noise to a source language text to obtain a noise added source language text, a noise added source language text, and noise of the noise added source language text. And a corpus constructing unit 13 for constructing a pseudo bilingual corpus in which the target language text corresponding to the source language text before being assigned is associated.
  • noise is added to the source language text, and the pseudo-translation corpus that associates the noise-added source language text with the target language text corresponding to the source language text before the noise addition is created. Be built. In this way, by constructing the bilingual corpus in which the source language text with noise is associated with the target language text corresponding to the source language text before adding noise, using such a bilingual corpus, for example, natural speech Even when noise such as a filler is included in the input, it is possible to appropriately derive the target language text corresponding to the source language text before noise addition.
  • a robust corpus (pseudo-translation corpus) can be constructed with respect to natural utterances containing noise, and non-fluent natural utterances containing noise. Can be translated with high accuracy.
  • the information generated by the translation data generating system 1 is used for translation, it is not necessary to correct the user's utterance content and input it into the translation model, and the user's utterance content is not changed. You can enter the translation model.
  • a speech recognition device is used to sequentially receive a user's utterance and make a rephrasing determination.
  • the translation data generation system 1 does not require a voice recognition device, and only needs to use the text information of the recognition result. As described above, in the translation data generation system 1 according to the present embodiment, it is possible to suppress the correction processing of the utterance content and the rewording determination processing, so that the processing load on the processing unit such as the CPU is reduced. It also plays a dynamic effect.
  • FIG. 7 is a table showing a translation example of this embodiment and a comparative example.
  • the upper part of FIG. 7 there is a lack of translation in the comparative example with respect to the natural utterance input including noise.
  • a natural utterance input including noise is translated in a state including noise in the comparative example, and desired translation cannot be performed.
  • the comparative example conventionally, it has been difficult to accurately translate a natural utterance containing noise.
  • the translation is performed in consideration of the pseudo bilingual corpus constructed by the translation data generation system 1 of the present embodiment, noise is naturally included. Translation errors are unlikely to occur even when uttered, and translation can be performed with high accuracy.
  • the translation data generation system 1 includes a translation model learning device 50 that learns a translation model using a pseudo bilingual corpus. By learning the translation model based on the constructed corpus, it is possible to translate the natural utterance containing noise with higher accuracy.
  • the translation data generation system 1 includes a noise model learning device 40 that learns a noise model related to noise addition to a source language text by using training data that is a source language text group including noise, and the noise addition unit 12 Adds noise to the source language text using a noise model.
  • a noise model is learned based on a source language text group that includes noise in advance, and noise is added based on the noise model, so that noise that is likely to be actually included is easily added. The accuracy can be further improved.
  • the noise imparting unit 12 imparts a noise label indicating the type of noise to each word of the source language text, and replaces the noise label with the word corresponding to the noise label. Add noise to linguistic text. Since the noise label corresponding to each word of the source language text is added and then the word (noise) corresponding to the noise label is derived, it is possible to ensure the easiness and validity of the noise addition.
  • the noise adding unit 12 derives a plurality of patterns of words to be replaced with respect to one noise label, and obtains a plurality of patterns of noise adding source language text from one source language text. As a result, it is possible to efficiently enhance the pseudo bilingual corpus from one source language text and further improve the translation accuracy.
  • the noise adding unit 12 derives a plurality of patterns of noise labels corresponding to each word, and obtains a plurality of patterns of noise-added source language text from one source language text. As a result, it is possible to efficiently enhance the pseudo bilingual corpus from one source language text and further improve the translation accuracy.
  • the noise adding unit 12 adds a noise label according to the characteristics of each word in the source language text. This makes it possible to appropriately give each word a noise label relating to noise that is likely to be included in association with each word.
  • the noise adding unit 12 adds a noise label according to at least one of a morpheme, a part of speech, and a word reading, which are characteristics of each word in the source language text. This makes it possible to appropriately give each word a noise label relating to noise that is likely to be included in association with each word.
  • the noise adding unit 12 samples the noise label according to the probability distribution of each noise level based on the score of each noise label output from the noise model with the feature of each word of the source language text as an input. , Determine the noise label added to the source language text.
  • the noise label For example, it is possible to give a noise label having a high score output from the noise model, and it is possible to appropriately give each word a noise label relating to noise that is likely to be included in association with each word.
  • the translation data generation device 10 described above may be physically configured as a computer device including a processor 1001, a memory 1002, a storage 1003, a communication device 1004, an input device 1005, an output device 1006, a bus 1007, and the like.
  • the word “device” can be read as a circuit, device, unit, or the like.
  • the hardware configuration of the translation data generating device 10 may be configured to include one or a plurality of each device illustrated in the figure, or may be configured not to include some devices.
  • Each function in the translation data generation device 10 causes a predetermined software (program) to be loaded on hardware such as the processor 1001 and the memory 1002, so that the processor 1001 performs an operation to perform communication by the communication device 1004 and the memory 1002. It is realized by controlling the reading and/or writing of data in the storage 1003.
  • a predetermined software program
  • the processor 1001 operates an operating system to control the entire computer, for example.
  • the processor 1001 may be configured by a central processing unit (CPU) including an interface with peripheral devices, a control device, a calculation device, a register, and the like.
  • CPU central processing unit
  • the control function of the noise adding unit 12 and the like of the translation data generating device 10 may be realized by the processor 1001.
  • the processor 1001 reads a program (program code), a software module and data from the storage 1003 and/or the communication device 1004 into the memory 1002, and executes various processes according to these.
  • a program program code
  • a program that causes a computer to execute at least part of the operations described in the above-described embodiments is used.
  • the control function of the noise adding unit 12 and the like of the translation data generating device 10 may be realized by a control program stored in the memory 1002 and operated by the processor 1001, and other function blocks are similarly realized. Good.
  • the various processes described above are executed by one processor 1001, they may be executed simultaneously or sequentially by two or more processors 1001.
  • the processor 1001 may be implemented by one or more chips.
  • the program may be transmitted from the network via an electric communication line.
  • the memory 1002 is a computer-readable recording medium, and is composed of, for example, at least one of ROM (Read Only Memory), EPROM (Erasable Programmable ROM), EEPROM (ElectricallyErasable Programmable ROM), RAM (Random Access Memory), and the like. May be done.
  • the memory 1002 may be called a register, a cache, a main memory (main storage device), or the like.
  • the memory 1002 can store an executable program (program code), a software module, etc. for implementing the wireless communication method according to the embodiment of the present invention.
  • the storage 1003 is a computer-readable recording medium, for example, an optical disc such as a CD-ROM (Compact Disc ROM), a hard disc drive, a flexible disc, a magneto-optical disc (for example, a compact disc, a digital versatile disc, a Blu-ray disc). (Registered trademark) disk), smart card, flash memory (eg, card, stick, key drive), floppy (registered trademark) disk, magnetic strip, and the like.
  • the storage 1003 may be called an auxiliary storage device.
  • the storage medium described above may be, for example, a database including the memory 1002 and/or the storage 1003, a server, or another appropriate medium.
  • the communication device 1004 is hardware (transmission/reception device) for performing communication between computers via a wired and/or wireless network, and is also called, for example, a network device, a network controller, a network card, a communication module, or the like.
  • the input device 1005 is an input device (for example, a keyboard, a mouse, a microphone, a switch, a button, a sensor, etc.) that receives an input from the outside.
  • the output device 1006 is an output device (for example, a display, a speaker, an LED lamp, etc.) that performs output to the outside.
  • the input device 1005 and the output device 1006 may be integrated (for example, a touch panel).
  • Each device such as the processor 1001 and the memory 1002 is connected by a bus 1007 for communicating information.
  • the bus 1007 may be configured with a single bus or different buses among devices.
  • the translation data generation device 10 includes hardware such as a microprocessor, a digital signal processor (DSP), an ASIC (Application Specific Integrated Circuit), a PLD (Programmable Logic Device), and an FPGA (Field Programmable Gate Array). May be included, and a part or all of each functional block may be realized by the hardware.
  • processor 1001 may be implemented with at least one of these hardware.
  • the translation data generation system may add a noise word (filler, stagnation, rewording, etc.) defined in advance to a random position of the source language text.
  • the word (noise) to be added to the random position may be randomly selected from noise word candidates, for example.
  • a noise model that randomly adds noise can be constructed as long as the noise word can be defined.
  • LTE Long Term Evolution
  • LTE-A Long Term Evolution-Advanced
  • SUPER 3G IMT-Advanced
  • 4G 5G
  • FRA Full Radio Access
  • W-CDMA Wideband Code Division Multiple Access
  • GSM Global System for Mobile Communications
  • CDMA2000 Code Division Multiple Access 2000
  • UMB Universal Mobile Broad-band
  • IEEE 802.11 Wi-Fi
  • IEEE 802.16 WiMAX
  • IEEE 802.20 UWB (Ultra-Wide) Band
  • Bluetooth registered trademark
  • a system using other appropriate systems and/or a next-generation system extended based on the above.
  • Information that has been input and output may be stored in a specific location (for example, memory), or may be managed in a management table. Information that is input/output may be overwritten, updated, or added. The output information and the like may be deleted. The input information and the like may be transmitted to another device.
  • the determination may be performed by a value represented by 1 bit (whether 0 or 1), may be performed by a Boolean value (Boolean: true or false), and may be performed by comparing numerical values (for example, a predetermined value). Value comparison).
  • the notification of the predetermined information (for example, the notification of “being X”) is not limited to the explicit notification, and is performed implicitly (for example, the notification of the predetermined information is not performed). Good.
  • software, instructions, etc. may be transmitted and received via a transmission medium.
  • the software may use wired technologies such as coaxial cable, fiber optic cable, twisted pair and digital subscriber line (DSL) and/or wireless technologies such as infrared, wireless and microwave to websites, servers, or other When transmitted from a remote source, these wireline and/or wireless technologies are included within the definition of transmission medium.
  • wired technologies such as coaxial cable, fiber optic cable, twisted pair and digital subscriber line (DSL) and/or wireless technologies such as infrared, wireless and microwave to websites, servers, or other
  • the information, signals, etc. described herein may be represented using any of a variety of different technologies.
  • data, instructions, commands, information, signals, bits, symbols, chips, etc. that may be referred to throughout the above description include voltage, current, electromagnetic waves, magnetic fields or magnetic particles, optical fields or photons, or any of these. May be represented by a combination of
  • information, parameters, etc. described in this specification may be represented by absolute values, may be represented by relative values from predetermined values, or may be represented by other corresponding information. ..
  • User terminals are defined by those skilled in the art as mobile communication terminals, subscriber stations, mobile units, subscriber units, wireless units, remote units, mobile devices, wireless devices, wireless communication devices, remote devices, mobile subscriber stations, access terminals, It may also be referred to as a mobile terminal, wireless terminal, remote terminal, handset, user agent, mobile client, client, or some other suitable term.
  • determining may encompass a wide variety of actions.
  • “Judgment” and “decision” are, for example, calculating, computing, processing, deriving, investigating, looking up (e.g., table, database or another). (Search in data structure), ascertaining (ascertaining) can be regarded as “judgment” and “decision”.
  • “decision” and “decision” include receiving (eg, receiving information), transmitting (eg, transmitting information), input (input), output (output), access (accessing) (for example, accessing data in a memory) may be regarded as “judging” and “deciding”.
  • judgment and “decision” are considered to be “judgment” and “decision” when things such as resolving, selecting, choosing, selecting, establishing, and comparing are done. May be included. That is, the “judgment” and “decision” may include considering some action as “judgment” and “decision”.
  • the phrase “based on” does not mean “based only on,” unless expressly specified otherwise. In other words, the phrase “based on” means both "based only on” and “based at least on.”
  • any reference to that element does not generally limit the amount or order of those elements. These designations may be used herein as a convenient way of distinguishing between two or more elements. Thus, references to the first and second elements do not imply that only two elements may be employed therein, or that the first element must precede the second element in any way.

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Abstract

L'invention concerne un système de génération de données de traduction (1) qui comprend une unité d'association de bruit (12) qui associe un bruit à un texte en langue source pour produire un texte en langue source associé à un bruit, et une unité de construction de corpus (13) qui construit un corpus pseudo-parallèle dans lequel le texte en langue source associé à un bruit, et le texte en langue cible correspondant au texte en langue source du texte en langue source associé à un bruit avant l'association à un bruit, sont corrélés.
PCT/JP2019/039337 2019-02-12 2019-10-04 Système de génération de données de traduction WO2020166125A1 (fr)

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CN113378586A (zh) * 2021-07-15 2021-09-10 北京有竹居网络技术有限公司 语音翻译方法、翻译模型训练方法、装置、介质及设备
CN114742076A (zh) * 2022-04-11 2022-07-12 网易有道信息技术（北京）有限公司 用于生成训练数据的方法、训练方法、设备及存储介质

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JP2018055671A (ja) * 2016-09-21 2018-04-05 パナソニックＩｐマネジメント株式会社 換言文識別方法、換言文識別装置及び換言文識別プログラム

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JP2018055671A (ja) * 2016-09-21 2018-04-05 パナソニックＩｐマネジメント株式会社 換言文識別方法、換言文識別装置及び換言文識別プログラム

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IMADE, MASAHIRO ET AL.: "Automatic corpora generation applied to neural machine translation", THE 31ST ANNUAL CONFERENCE OF THE JAPANESE SOCIETY FOR ARTIFICIAL INTELLIGENCE , 2017, 26 May 2017 (2017-05-26), pages 1 - 4 *
OHTA, KENGO ET AL.: "Construction of Language Model with Fillers from Corpus without Fillers", IPSJ SIG TECHNICAL REPORT., vol. 2007, no. 75, 21 July 2007 (2007-07-21), pages 1 - 6 *

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113378586A (zh) * 2021-07-15 2021-09-10 北京有竹居网络技术有限公司 语音翻译方法、翻译模型训练方法、装置、介质及设备
CN113378586B (zh) * 2021-07-15 2023-03-28 北京有竹居网络技术有限公司 语音翻译方法、翻译模型训练方法、装置、介质及设备
CN114742076A (zh) * 2022-04-11 2022-07-12 网易有道信息技术（北京）有限公司 用于生成训练数据的方法、训练方法、设备及存储介质

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