Machine translation apparatus, machine translation method and computer program product for machine translation

According to one embodiment, a machine translation apparatus includes an input device which inputs a first language sentence; an additional information acquisition unit which acquires a first user or a current location of the first language sentence as a first additional information; a reference data storage device which stores second language reference data that are the relationships between second language sentences and at least one of a second user and a second user usage location of the second language sentences as a second additional information; a text data acquisition unit which acquire second language text data from the second language reference data including second additional information being the same at least one part of the first additional information; and a translation unit which translates the first language sentence to a second language sentence by using the second language text data.

CROSS REFERENCE TO RELATED APPLICATION

This application is based upon and claims the benefit of priority from Japanese Patent Application No. 2011-031240, filed on Feb. 16, 2011; the entire contents of which are incorporated herein by reference.

FIELD

Embodiments described herein relate generally to a machine translation apparatus and associated methods.

BACKGROUND

The popularity of computers continues to grow as performance increases and broadband infrastructures proliferate. Machine translation apparatuses and services that translate input text documents of a source language to text documents of a target language are becoming more popular. Methods for improving accuracy of machine translations are proposed to select translation dictionaries in response to usage scenes, for example, “shopping” and “vehicle” set in advance by a user.

However such methods merely react to the usage scenes classified off-line in advance.

DETAILED DESCRIPTION

According to one embodiment, a machine translation apparatus includes an input device configured to input a first language sentence; an additional information acquisition unit configured to acquire at least one of a first user and a current location of the first language sentence as a first additional information; a reference data storage device configured to store second language reference data that are the relationships between second language sentences and at least one of a second user and a second user usage location of the second language sentences as a second additional information; a text data acquisition unit configured to acquire second language text data from the second language reference data including second additional information being the same at least one part of the first additional information; and a translation unit configured to translate the first language sentence to a second language sentence by using the second language text data.

One Embodiment

A machine translation apparatus according to an embodiment translates an English text document (example of a source language) to a Japanese text document (example of a target language). When a Japanese user uses the Japanese text document, the machine translation apparatus acquires the Japanese user's profile data and current (or usage) location as additional information. The apparatus learns adaptive model of machine translation being suitable for the additional information by using reference data. The reference data is that the Japanese text document corresponds to the additional information.

When the apparatus acquires the English text document inputted by an English user and additional information of the English text document, the apparatus obtains Japanese reference data including similar additional information from a reference data storage unit. The apparatus generates an adaptive model representing a trend of machine translation from the Japanese text document of the acquired Japanese reference data.

Methods of generating an adaptive model of machine translation online dynamically can make the apparatus learn the adaptive model flexibly corresponding to a Japanese user's usage scenes.

The apparatus translates an inputted English sentence by a conventional method and calculates a plurality of candidates for the translated sentence and the confidence scores of the candidates.

The apparatus updates the confidence scores by using the adaptive model generated online dynamically and outputs a Japanese sentence as the final result of the machine translation.

(Entire of the Machine Translation Apparatus)

FIG. 1shows the entire machine translation apparatus according to an embodiment. The apparatus includes an input device101for inputting an English sentence as a source language sentence, an additional information acquisition unit102for acquiring at least one of English user and current location of the English sentence as an English additional information, a reference data storage device103for storing Japanese reference data that are the relationships between Japanese sentences and at least one of users and usage locations of the Japanese sentences as Japanese additional information, a text data acquisition unit104for acquiring Japanese text data from the Japanese reference data including Japanese additional information being the same at least one part of the English additional information, a translation unit105for translating the English sentence to a Japanese sentence by using Japanese text data, and an output device106for outputting the Japanese sentence.

The translation unit105translates the English sentence to the Japanese sentence by using translation dictionary storage device107. The translation unit105includes a candidate acquisition unit108for acquiring a plurality of candidates for translated sentence and confidence scores of the candidates, an adaptive model learning unit109for learning adaptive model that represents a trend of machine translation by using the Japanese text data acquired by the text data acquisition unit104, an adaptive translation unit110for updating the confidence scores of the candidates by using the adaptive model learned by the adaptive model learning unit109.

The input device101inputs English text sentence of source language. The input device101can be a keyboard, pointing device, handwritten character recognition, optical character reader (OCR), speech recognition, etc.

The additional information acquisition unit102acquires at least one of English user's profile data and current location of the English sentence as an English additional information. The English user's profile data represent the English user's gender, age, occupation, interest, language, etc. The English user inputs the English user's profile data to the machine translation apparatus once when the English user is registered.

The machine translation apparatus further includes Global Positioning System (GPS) sensor that acquires current location of the English sentence.FIG. 2Ashows additional information when the input unit inputs an English sentence “It looks good.” The additional information are acquired gender, year of birth and language as the English user's profile data. The additional information are acquired latitude and longitude as the current location.

The additional information acquisition unit102analyses the English user's profile data and current location to acquire further additional information. For example, when the unit102checks the latitude and the longitude with real map data, the unit102acquires the English user's current facility category “restaurant”, city name, etc as the other additional information (shown inFIG. 2B). The unit102calculates the user's generation (or age group) “10” based on the user's birth year (1993) and current year (2011).

The candidate acquisition unit108translates the English text sentence to a Japanese text sentence. Concretely, the unit108translates the English text sentence by using translation dictionary storage device107and outputs a plurality of candidates for translated sentence (candidate group for translated sentence) and confidence scores of the candidates that represents likelihoods of the candidates. The machine translation uses one or more of a transfer method, an example-based method, a statistics-based method, an intermediate-language method, etc.

FIG. 3shows candidates for translated Japanese sentence and confidence scores of the candidates when the input device101inputs the English sentence “It looks good.”

The reference data storage device103stores Japanese reference data that are the relationships between Japanese sentences and at least one of users and usage locations of the Japanese sentences as Japanese additional information. The reference data is utilized for learning data when the adaptive model learning unit109calculates the adaptive model.

FIG. 4shows examples of Japanese reference data. The Japanese reference data is utilized as another reference data of another Japanese user's machine translation apparatus.

For example, when a native Japanese user inputted a native Japanese text sentence to another machine translation apparatus, the relationship between the native Japanese text sentence and at least one of the native Japanese user and usage locations of the native Japanese text sentences, as Japanese additional information, is utilized for native Japanese reference data, as past history of the native Japanese reference data. The native Japanese reference data is stored by the share server. The share server is located in Social Networking Service (SNS) server. In this way, multiple native languages reference data are easily stored in SNS server. The device103acquires the native Japanese reference data from the share server in SNS server.

The text data acquisition unit104utilizes a plurality of candidates for the translated sentence acquired by the unit108and the English additional information corresponding to the candidates, and acquires Japanese text sentence for learning the adaptive model from the Japanese reference data stored by the device103.

First, the unit104acquires Japanese reference data including Japanese additional information being the same at least one part of the English additional information. In this case, the unit104determines the identity between Japanese additional information and English additional information based on not only all additional information but also “gender”, “generation (or age group)” and “category of facilities”.

Second, the unit104calculates the degree of the similarity between the Japanese text sentences of the Japanese reference data and the candidates for translated sentence. When the degree is more than a threshold value, the unit104outputs the Japanese text sentence to the unit109.

In the case ofFIGS. 2A and 2B, the unit104acquires the Japanese reference data having Gender is “Woman”, Generation is “10”, and, Category of facilities is “Restaurant” from the device103.FIG. 5Ashows Japanese text data acquired from the device103.

The unit104calculates the degree of the similarity between the Japanese text data shown inFIG. 5Aand the candidates for translated sentence shown inFIG. 3.FIG. 5Bshows the calculated degree of the similarity.

The unit104acquires the Japanese text data having the degree shown inFIG. 5Bis more than a predetermined threshold value and outputs the Japanese text data to the unit109. TheFIG. 5Cshows the Japanese text data outputted by the unit104, when the threshold value is set as “0.4”.

The method of calculating the degree of the similarity between Japanese text data and the candidates for translated sentence is described. The method uses Edit Distance, Word Error Rate, Translation Error Rate, Bilingual Evaluation Understudy (BLEU), or another barometer using n-gram information. In the embodiment, the degree of the similarity is the value that the degree of the coincidence between character 3(tri)-grams of the Japanese text sentence and 3-grams of the candidates is divided by the numbers of 3-grams of the both sentences.

For example, when “—∘ (Koreoishisoudane-.)” and “—∘ (oishisoudane-)” is separated to 3-grams, the first sentence represents “(koreo)(reoi)(oishi)(ishiso)(shisou)(souda)(udane)(dane-)—∘ (ne-.)”, and the second sentence represents “(oishi)(ishiso)(shisou)(souda)(udane)! (dane!)”.

The two sentences have 5 common 3-grams, that is, “(oishi)”,(ishiso)”, “(shisou)”, “(souda)”, and “(udane)”. The degree of the similarity is “ 5/10=0.5”.

The unit104calculates the degree of the similarity between each Japanese text sentence and the candidates for translated sentence using the above method. The maximum value of the calculated degree of the similarity is the final degree of the similarity on the Japanese text sentence.

In this embodiment, the unit104determines the identity between Japanese additional information and English additional information based on, for example, “gender”, “generation (or age group)” and “category of facilities”, but is not limited to, based on the all additional information.

The adaptive model learning unit109learns the adaptive model that represents a trend of machine translation by using the Japanese text data acquired by the unit104. In the embodiment, the adaptive model is Word Bigram Language Model learned by adaptive model sentence group. The Word Bigram Language Model may be used as well, including those described in Philipp Koehn, “Statistical Machine Translation,” Cambridge University Press, 2010, Chapter 7, the entire contents of which is incorporated herein by reference.

FIG. 6shows the adaptive model of the word bigram represented by the logarithmic probability.

Examples of other adaptive models include N-gram Language model, Hidden Markov Model, Maximum Entropy Model, Weighted Finite State Transducer, Conditional random field, Log-linear model, or another probability model, or Term Frequency—Inverse Document Frequency, or another method of extracting characteristic word.

The adaptive translation unit110updates the confidence stores of candidates for translated sentence acquired by the unit108, by using the adaptive model learned by the unit109. For example, the unit110acquires the average of the confidence score acquired by the unit108and the adaptive score calculated by the adaptive model, as the confidence score after updating.

FIG. 7Ashows the case of calculating the adaptive score of the candidates shown inFIG. 3using the adaptive model. The adaptive score of the sentence “—! (What a delicious food!)” shown in ID8ofFIG. 8is calculated “0.310+0.147=0.457”, using the adaptive model of “((oishisou), —)” and “(—, !)” shown inFIG. 6. The unit110calculates the confidence score after updating, using the confidence score before updating and adaptive score shown inFIG. 7A.FIG. 7Bshows a list of the candidates for translated sentence in order of large confidence score before updating. In the case ofFIG. 7B, the confidence score after updating of ID1“(It looks good.)” is “(0.358+0.093)/2=0.226”, and the confidence score after updating of ID8“—! (What a delicious food!)” is “(0.268+0.457)/2=0.363”.

As mentioned above, when the confidence score is updated by using the adaptive score, the confidence score of the candidate that is suitable for the additional information having Gender is “Woman”, Generation is “10”, and, Category of facilities is “Restaurant” becomes bigger.

Updating the confidence score by using the adaptive model learned online dynamically can acquire the result of the machine translation being suitable for at least one of the user's profile data and the current location of the current additional information. For example, when “Man” inputs “It looks good.”, the confidence score of “—! (What a delicious food!)” shown in ID7ofFIG. 7Abecomes the biggest. When “It looks good.” is inputted in “clothing store”, the confidence score of “—! (It's lovely!)” shown in ID17ofFIG. 7Abecomes the biggest.

The output device106outputs the candidates having the biggest confidence score updated by the unit110, as the final result of the machine translation. The output style of the device106is an image output of a display, a print output of a printer, a speech-synthesis output of a speech-synthesis, etc.

FIG. 8illustrates a flow chart of the operation of the machine translation apparatus. The device101input an English text sentence of source language (Step81). The unit102acquires the English user's profile data and current location as additional information on the inputted English text sentence (Step82). The unit108translates the inputted English text sentence to Japanese and acquires a plurality of candidates for translated sentence and confidence scores of the candidates (Step83). The unit104acquires Japanese text data using learning of adaptive mode, from Japanese reference data stored by the device103, by using the candidates acquired by unit108and English additional data (Step84).

The unit109learns the adaptive model by using the Japanese text data acquired by the unit104(Step85). The adaptive translation unit110updates the confidence scores of the candidates by using the adaptive model learned by the unit109. The output device106outputs the candidates having the biggest confidence score updated by the unit110, as the final result of the machine translation (Step87).

FIG. 9illustrates a detail flow chart of Step84. The unit104acquires the Japanese reference data including Japanese additional information being the same at least one part of the English additional information of the English text sentence, from the device103(Step91). If the Japanese reference data is acquired (reference “Yes” of Step92), then the unit104selects one Japanese text data P from the Japanese reference data (Step93). The unit104calculates the degree of the similarity between the Japanese text data P and the candidate group C for translated sentence (Step94). If the calculated degree is more than or equal to the threshold value (reference “Yes” of Step95), the unit104adds the Japanese text data P to the learning data of the adaptive model (Step96). On the other hand, if the calculated degree is less than the threshold value (reference “No” of Step95), the unit104checks the calculation on the other Japan text data P (Step97). If calculating the degree of the similarity according to the all Japanese text P is completed (reference “Yes” of Step97), the unit104outputs the acquired the Japanese learning data to the unit109(Step98).

The machine translation apparatus according to the embodiment online dynamically generates the adaptive model representing a trend of machine translation from the Japanese text data corresponding to the additional information. The apparatus can translate suitably for the English user's profile and current location.

The method of automatically generating the adaptive model from the additional information by acquired by the machine translation apparatus can save the trouble that the user preliminarily selects the usage scene.

The apparatus uses the reference data, including Japanese sentences of another user and usage location of the other user, stored in the share server or SNS server. The apparatus uses native Japanese sentences and usage locations of the native Japanese user.

(First Variation of the Embodiment)

The device103according to a first variation of the embodiment furthermore stores the English sentence inputted by the device101and the English additional information acquired by the unit102, as the English reference data. The English reference data is used for translating Japanese to English.

The English reference data is stored in the share server or SNS server via network. The English reference data that is used for learning an adaptive model of Japanese user is collected without cost.FIG. 10shows a plurality of languages reference data stored by the device103.

(Second Variation of the Embodiment)

To acquire the additional information uses sensing data including, for example, an acceleration sensor, a gyro sensor, environment light sensor, blood pressure sensor, etc. and picture images of camera device.

The method of extracting information on user's behavior can be used as well, including those described in US Patent Application Publication 2010/0070235, the entire contents of which are incorporated herein by reference. The method extracts information on user's behavior, for example getting in a car, riding in a train, running, walking, or standing. The extracted information is used for the additional information.

When the machine translation apparatus has a communication unit, the apparatus acquires the base-station ID as the additional information on usage place of the user.

(Third Variation of the Embodiment)

In the embodiment, the unit108acquires a plurality of candidates for the translated sentence and the confidence scores of the candidates and the unit110updates the confidence scores. On the other hand, in a third variation of the embodiment, In-Domain Language Model can be used as well, including those described in Philipp Koehn, “Experiments in Domain Adaptation for Statistical Machine Translation”, Proceedings of the Second Workshop on Statistical Machine Translation, Prague, Czech Republic, June 2007, p. 224-227, the entire contents of which is incorporated herein by reference. Using the In-Domain Language Model as the adaptive model learned by the unit109can directly calculate the final confidence score.

(Fourth Variation of the Embodiment)

The Japanese reference data stored by the device103is not only text format but also speech data format. When the adaptive model is learned, the unit104extracts the Japanese text data from the speech data with speech recognition.

(Fifth Variation of the Embodiment)

In the embodiment, the unit109learns one adaptive model. On the other hand, in a fifth variation of the embodiment, the unit109learns a plurality of adaptive models from the reference data. For example, the unit109learns each adaptive model from the reference data of the same age group or the reference data of the same location. The unit109calculates the confidence score by adding the learned adaptive models with weight.

According to the machine translation apparatus of at least one embodiment described above, the apparatus is able to translate in response to current usage scene of user.

The flow charts of the embodiments illustrate methods and systems according to the embodiments. It will be understood that each block of the flowchart illustrations, and combinations of blocks in the flowchart illustrations, can be implemented by computer program instructions. These computer program instructions can be loaded onto a computer or other programmable apparatus to produce a machine, such that the instructions which execute on the computer or other programmable apparatus create means for implementing the functions specified in the flowchart block or blocks. These computer program instructions can also be stored in a non-transitory computer-readable memory that can direct a computer or other programmable apparatus to function in a particular manner, such that the instruction stored in the non-transitory computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart block or blocks. The computer program instructions can also be loaded onto a computer or other programmable apparatus/device to cause a series of operational steps/acts to be performed on the computer or other programmable apparatus to produce a computer programmable apparatus/device which provides steps/acts for implementing the functions specified in the flowchart block or blocks.