Patent Publication Number: US-2015081273-A1

Title: Machine translation apparatus and method

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is baaed upon and claims the benefit of priority from Japanese Patent Application No. 2013-194640, filed Sep. 19, 2013, the entire contents of which are incorporated herein by reference. 
     FIELD 
     Embodiments described herein relate generally to a machine translation apparatus and method that mechanically translate an original language sentence into an object language sentence via translation based on an interlanguage. 
     BACKGROUND 
     Recently, the machine translation technique for translating a sentence of a natural language (original language) to a sentence of another natural language (object language) by use of a computer has been developed and widely used. There is a broad range of languages for translation. Therefore, a multi-language translation system that realizes translations between languages is proposed. In the multi-language translation system, it requires a significant amount of development costs to develop a translation engine for every language, and it is not efficient. Under this context, an interlanguage system for first translating an original language into an interlanguage and then translating the interlanguage into an object language is proposed. 
     In the conventional interlanguage system, a preset natural language (for example, English) is used as an interlanguage. In this case, information held in the original sentence of the original language may be lost at the time of translation from the original language into the interlanguage. For example, it is assumed that Japanese is used as the original language, English is used as the interlanguage, German is used as the object language, and Japanese is translated into English and then translated into German. When a Japanese sentence “   ? (‘Hansu, ogenkidesuka?’)” is translated into English, an English sentence “How are you, Hans?” is obtained, and when the English sentence “How are you, Hans?” is translated into German, “Wie geht&#39;s dir, Hans?” is obtained. In this example, the nuance of polite expression contained in the Japanese sentence, that is to say a part of the information held in the original language sentence, is lost at the time of translation into English or an interlanguage and the above nuance is not reflected in the sentence of German that is the final object language. As a German sentence corresponding to the Japanese sentence “   ?”, the politer expression of “Wie geht&#39;s Ihnen, Hans?” is more appropriate. 
     As described above, in the conventional interlanguage system, a part of the information held in the original language sentence may be lost in some cases at the time of translation into the interlanguage and a problem of a decline in translation accuracy may occur. In the machine translation technique, it is required to make a highly accurate translation even when the interlanguage system is used. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram schematically showing a machine translation apparatus according to an embodiment. 
         FIG. 2  is a diagram showing an example of the result of analysis conducted by an original sentence analysis unit shown in  FIG. 1 . 
         FIG. 3  is a diagram showing an example of a selection model stored in a selection model storage unit shown in  FIG. 1 . 
         FIG. 4  is a diagram showing an example in which an output unit shown in  FIG. 1  displays the translation result on a display device. 
         FIG. 5  is a diagram showing another example in which the output unit shown in  FIG. 1  displays the translation result on the display device. 
         FIG. 6  is a diagram showing still another example in which the output unit shown in  FIG. 1  displays the translation result on the display device. 
         FIG. 7  is a flowchart showing an example of a machine translation process according to the embodiment. 
         FIG. 8  is a flowchart showing an example of an interlanguage selection process according to the embodiment. 
     
    
    
     DETAILED DESCRIPTION  
     In general, according to an embodiment, a machine translation apparatus for translating an original language into an object language via an interlanguage includes an input unit, an analysis unit, a storage unit, an interlanguage selection unit, a translation controller, and an output unit. The input unit is configured to input an original sentence of the original language. The analysis unit is configured to analyze the original sentence to generate analysis information. The storage unit is configured to store a selection model obtained by modeling characteristics of translation from the original language into a plurality of interlanguage candidates. The interlanguage selection unit is configured to select the interlanguage from the plurality of interlanguage candidates based on the analysis information and the selection model. The translation controller is configured to generate an object language sentence obtained by translating the original sentence into the object language via the interlanguage. The output unit is configured to output the object language sentence. 
     Various embodiments are explained hereinafter with reference to the accompanying drawings. The embodiments are directed to machine translation apparatuses which mechanically translate an original language into an object language via an interlanguage. In the embodiments, a case wherein the original language is Japanese and the object language is German is explained. The combination of the original language and object language is not limited to the example explained here and two given natural languages can be freely combined. 
       FIG. 1  schematically shows a machine translation apparatus  100  according to an embodiment. As shown in  FIG. 1 , the machine translation apparatus  100  includes an input unit  101 , an original sentence analysis unit (simply referred to as an analysis unit, hereinafter)  102 , an interlanguage selection unit  103 , a selection model storage unit (simply referred to as a storage unit, hereinafter)  104 , a translation engine unit  106 , a translation controller  107 , and an output unit  108 . 
     The translation engine unit  106  includes translation devices (also called translation engines)  105 . Each translation device  105  translates a natural language into another natural language. In the present embodiment, the translation engine unit  106  includes six translation devices  105 A,  105 B,  105 C,  105 D,  105 E, and  105 F. The translation device  105 A executes a translation from Japanese into English. The translation device  105 B executes a translation from Japanese into Chinese. The translation device  105 C executes a translation from Japanese into Korean. The translation device  105 D executes a translation from English into German. The translation device  105 E executes a translation from Chinese into German. The translation device  105 F executes a translation from Korean into German. 
     In the present embodiment in which the translation devices  105 A to  105 F can be used, English, Chinese and Korean can be used as the interlanguage at the time of translation from Japanese into German. In the following description, a natural language that can be used as the interlanguage is called an interlanguage candidate. That is, in the embodiment, English, Chinese and Korean are used as the interlanguage candidates. 
     The translation device  105 , which corresponds to one or each of the translation devices  105 A to  105 F, performs a machine translation. As the machine translation, rule based machine translation, example based machine translation, statistical machine translation and the like that are conventionally known, can be appropriately used. The above translations are generally widely known and a detailed explanation is therefore omitted. The translation device  105  may be realized by translation based on a human translation. 
     The input unit  101  receives input of the original sentence of the original language and temporarily stores the original sentence. The original sentence of the original language may be listed as the original language sentence. The input original sentence is transmitted to the analysis unit  102  and the translation controller  107 . 
     The analysis unit  102  analyzes the original sentence received from the input unit  101  to generate analysis information. For example, the analysis is realized by use of a morphological analyzer that separates the original sentence into morphemes and obtains part of speech attributes of the morphemes, a syntactic analyzer that attains the grammatical relationship of the original sentence by using syntagmatic analysis, dependency structure analysis, or the like. The analyzing methods are generally widely known, and therefore a detailed explanation thereof is omitted. The analysis may be realized by use of another given analyzing method. 
     The analysis information contains information concerning at least one original sentence feature. Examples of the original sentence feature contain the sentence type, tense, voice, honorific expression, ambiguity of words, sentence structure, character type and the like of the original sentence. As one example, the sentence type can be classified into three types; a declarative sentence, an interrogative sentence, or an imperative sentence. In this case, the analysis information contains information indicating the sentence type of a declarative, an interrogative, or an imperative sentence in which the original sentence is written. 
       FIG. 2  shows the result of analysis obtained by the analysis unit  102 . Specifically,  FIG. 2  shows the result of analysis of the original sentence of (Japanese sentence) “ ?”. In  FIG. 2 , feature names used for identifying original sentence features and values assigned to the original sentence features are shown. When the original sentence contains an applicable original sentence feature, a value “1” is assigned, otherwise a value “0” is assigned. In this example, for simplifying the explanation, identifiers are attached to the respective feature names. In the following description, the original sentence feature of identifier #i is entered as original sentence feature #i. In this case, i is an integral number not smaller than 1 and not larger than N and N indicates the number of original sentence features. For example, original sentence feature #1 indicates whether or not the sentence type of the original sentence is a declarative sentence. When the original sentence is described in a declarative sentence form, the value “1” is assigned to original sentence feature #1. 
     In the example of  FIG. 2 , the fact that the sentence type is an interrogative sentence, the tense is a present tense and the honorific expression is a polite expression is obtained as the result of analysis of the Japanese sentence “   ?”. Therefore, the value “1” is assigned to original sentence features #2, #4, #11 and the value “0” is assigned to the other original sentence features. 
     The values assigned to the original sentence features are not limited to an example of binary values as shown in  FIG. 2 , and may be real numbers. Furthermore, a character string may be assigned to an original sentence feature. In an example having the sentence type of original sentence feature #1, the value “0” or character string “declarative sentence” may be assigned if the sentence type is a declarative sentence, the value “1” or character string “interrogative sentence” may be assigned if the sentence type is an interrogative sentence and the value “2” or character string “imperative sentence” may be assigned if the sentence type is an imperative sentence. 
     The example in which the original sentence as shown in  FIG. 2  is analyzed in the sentence unit is not limited, and the analysis may be made in the given clause unit or given morpheme unit (partial series of morphemes). 
     The storage unit  104  stores a selection model obtained by modeling characteristics of translation from the original language into interlanguage candidates. The characteristic of translation indicates the degree to which the original sentence feature can be transmitted when a certain natural language is translated into another natural language, and can be expressed by use of the transmission coefficient with respect to the original sentence feature. In the present embodiment, the selection model contains the transmission coefficients with respect to the original sentence features for the respective interlanguage candidates. 
       FIG. 3  shows an example of a selection model according to the present embodiment. The selection model shown in  FIG. 3  contains the transmission coefficients with respect to the original sentence features for English, Chinese and Korean. The original sentence features of the selection model are set in correspondence to the original sentence features of analysis information. That is, an identifier shown in  FIG. 3  corresponds to the identifier shown in  FIG. 2 . For example, original sentence feature #11 indicates whether the honorific expression is a polite expression or not. 
     In the example of  FIG. 3 , the transmission coefficient for original sentence feature #11 is set at 0.2 in the case of English, 0.2 in the case of Chinese, and 0.8 in the case of Korean. The reason why the transmission coefficient for original sentence feature #11 is set at a small value in the cases of English and Chinese is that expressions corresponding to honorific expressions are not present in the English and Chinese languages. On the other hand, the reason why the transmission coefficient for original sentence feature #11 is set at a large value in the case of Korean is that honorific expressions similar to those of Japanese are present in the Korean language, and the Korean language can sufficiently faithfully transmit honorific expressions contained in the original sentence. 
     If the selection model can express the transmission coefficients for the original sentence features, the form thereof can be freely set. For example, an evaluation set of original language sentences containing a specified original sentence feature is prepared, and the translation accuracy with respect to the interlanguage candidate may be set as the transmission coefficient. The translation accuracy can be quantified based on a machine evaluation or manual evaluation. The machine evaluation can be performed based on an evaluation method such as BLEU (Bilingual Evaluation Understudy), which is a machine evaluation method for measuring the correspondence degree with respect to a reference translated sentence, NIST (National Institute of Standards and Technology) or WER (Word Error Rate). 
     The interlanguage selection unit  103  selects an interlanguage used for translation from interlanguage candidates based on the analysis information generated from the analysis unit  102  and the selection model stored in the storage unit  104 . Specifically, the interlanguage selection unit  103  calculates scores of the respective interlanguage candidates based on the analysis information and selection model and selects the interlanguage candidate having the largest score as an interlanguage used for translation. The score in the present embodiment indicates the appropriateness as the interlanguage. Thus, in the present embodiment, interlanguages used for translation are switched according to the input original sentence. Therefore, it becomes possible to transmit information held in the original sentence to a translated sentence and, as a result, the translation accuracy can be enhanced. 
     The translation controller  107  communicates with the translation engine unit  106  including the translation devices  105 . The translation controller  107  controls the translation engine unit  106  to generate an object language sentence (that is, a translated sentence) obtained by translating the original sentence received from the input unit  101  into an object language via the interlanguage selected by the interlanguage selection unit  103 . Specifically, the translation engine unit  106  translates the original sentence into an object language by use of a combination of the translation device  105  that translates the original language into a selected interlanguage and the translation device  105  that translates the selected interlanguage into the object language. The thus generated object language sentence is supplied to the output unit  108 . 
     The translation engine unit  106  is not limited to an example in which it is mounted on the machine translation apparatus  100 , and may be provided on the exterior of the machine translation apparatus  100 . If the translation engine unit  106  is provided on the exterior of the machine translation apparatus  100 , it may be realized by a translation service on the Web, for example. In this case, the translation controller  107  communicates with the translation engine unit  106  via a communication network such as the Internet and requests the translation engine unit  106  to make a translation via translation based on the interlanguage selected by the interlanguage selection unit  103 . 
     The output unit  108  presents the object language sentence generated from the translation controller  107  to the user. The output unit  108  can present comment information including the original language sentence, interlanguage sentence, and a message that indicates whether analysis information is reflected or not, together with the object language sentence. The interlanguage sentence is an intermediate translated sentence obtained by translating the original sentence into a selected interlanguage. For example, presentation of the object language sentence may be performed by any one of methods for image outputting by use of a display device (not shown), print outputting by use of a printer device (not shown), speech outputting by use of a speech synthesizing device and the like. Also, the above outputting methods are previously incorporated, the outputting methods can be interchangeably used as required, and two or more outputting methods can be used in combination. 
       FIG. 4  shows an example of a display screen when the output unit  108  outputs an object language sentence to the display device. As shown in  FIG. 4 , the display screen includes a field  401  that displays an input original sentence, a field  402  that displays the object language sentence, and a field  403  that displays an interlanguage sentence. 
       FIG. 5  shows another example of the display screen when the output unit  108  outputs the object language sentence to the display device. As shown in  FIG. 5 , the display screen includes a field  501  that displays the input original sentence, a field  502  that displays the translation result, and a field  503  that displays a message. In an example of  FIG. 5 , the message displayed in the field  503  indicates the possibility that the honorific expression of the original sentence is not reflected in the translated sentence. For example, the message is presented when the analysis unit  102  determines that the honorific expression is contained in the original sentence, but the transmission coefficient with respect to the honorific expression in the selected interlanguage is lower than a threshold value. In such a case, the interlanguage selection unit  103  may select a plurality of interlanguages, the translation controller  107  may generate object language sentences corresponding to the interlanguages, and the output unit  108  can display the object language sentences as translation candidates in the field  502 . The translation candidates are rearranged and displayed in the order in which the scores calculated by the interlanguage selection unit  103  are higher. In addition, as shown in  FIG. 6 , interlanguages used for acquiring the translation candidates may be displayed in correspondence to the translation candidates, and information indicating the propriety of a message displayed in the field  503  may be displayed in the field  502 . 
     The machine translation apparatus  100  according to the present embodiment selects an interlanguage from interlanguage candidates based on the analysis result of the original sentence and the selection model. Therefore, a translation can be executed by use of an interlanguage suitable for translation of the original sentence. As a result, a highly accurate translation can be executed even if a translation device that executes a translation from the original language into the object language, a translation dictionary between the original language and the object language, and a linguistic knowledge of the object language cannot be utilized. 
     Next, the machine translation process of the machine translation apparatus  100  is explained. 
       FIG. 7  shows the procedure of the machine translation process according to the present embodiment. In step S 701  of  FIG. 7 , the input unit  101  receives input of the original sentence expressed by an original language. In step S 702 , the interlanguage selection unit  103  selects an interlanguage used for translation of the original sentence from interlanguage candidates by an interlanguage selection process. The interlanguage selection process will be described in detail later. 
     In step S 703 , the translation controller  107  supplies the original sentence to the translation device  105  that translates the original language into the selected interlanguage and acquires an interlanguage sentence from this translation device  105 . In step S 704 , the translation controller  107  supplies the interlanguage sentence to the translation device  105  that translates the selected interlanguage into an object language and acquires an object sentence from this translation device  105 . 
     In step S 705 , the output unit  108  outputs the object language sentence acquired by the translation controller  107 . For example, the object language sentence is displayed on the display screen of the display device. As a result, the machine translation process is terminated. 
     Next, the interlanguage selection process shown in step S 702  is explained in detail with reference to  FIG. 8 . 
       FIG. 8  shows the procedure of the interlanguage selection process according to the present embodiment. In step S 801  of  FIG. 8 , the interlanguage selection unit  103  acquires interlanguage candidates that are natural languages usable for translation from the original language into the object language from the translation controller  107 . The translation controller  107  can determine the interlanguage candidates based on the translation devices  105  contained in the translation engine unit  106 . In step S 802 , the interlanguage selection unit  103  determines whether a plurality of interlanguage candidates are obtained or not. When only one interlanguage candidate is obtained, the interlanguage selection unit  103  selects the interlanguage candidate as an interlanguage and the interlanguage selection process is terminated. 
     If a plurality of interlanguage candidates are obtained, the process proceeds to step S 803 . In step S 803 , the interlanguage selection unit  103  acquires analysis information from the analysis unit  102 . The analysis unit  102  performs an analysis process such as morphologic analysis, syntagmatic analysis, and dependence structure analysis that are conventionally used and then generates analysis information. 
     In step S 804 , scores of the interlanguage candidates are calculated based on the analysis information and the selection model. As one example, score Z 1  of interlanguage candidate 1 is calculated according to the following equation (1): 
     
       
         
           
             
               
                 
                   
                     Z 
                     l 
                   
                   = 
                   
                     
                       ∑ 
                       
                         i 
                         = 
                         1 
                       
                       N 
                     
                      
                     
                       
                         φ 
                          
                         
                           ( 
                           
                             f 
                             i 
                           
                           ) 
                         
                       
                       × 
                       
                         w 
                         
                           l 
                           , 
                           i 
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   1 
                   ) 
                 
               
             
           
         
       
     
     where f i  indicates a value assigned to original sentence feature #i contained in the analysis information, w 1,i  indicates the transmission coefficient of interlanguage candidate 1 with respect to original sentence feature #i, N indicates the number of original sentence features and φ(·) indicates a given characteristic function. 
     In step S 805 , the interlanguage selection unit  103  selects one of the interlanguage candidates having the largest score as an interlanguage. As a result, the interlanguage selection process is terminated. 
     The interlanguage selection unit  103  may select a specified interlanguage when the analysis unit  102  fails to acquire the original sentence feature, or when the largest score is lower than a preset threshold value. The specified interlanguage may be automatically set by the machine translation apparatus  100 , or may be set from the exterior by the user of the machine translation apparatus  100 . 
     Next, the specific example of the machine translation process according to the present embodiment is explained. A case where the Japanese sentence of “   ?” is input and the sentence is translated into German is explained as an example. English, Chinese, and Korean are assumed as interlanguage candidates usable for translation from Japanese into German, and the selection model stores the transmission coefficients shown in  FIG. 3 . Also, it is assumed that analysis information shown in  FIG. 2  is acquired as the result of analysis of Japanese sentence of “   ?” by the analysis unit  102 . In the example of  FIG. 2 , the values of original sentence features #2, #4, #11 are “1” and the values of the other original sentence features are “0”. That is, f i =1 (i=2, 4, 11) and f i =0 (i=1, 3, 5, . . . , 10, 12, . . . , N). 
     The characteristic function φ(·) shown in Equation (1) is used for converting a value of the original sentence feature into a given numerical value. In this example, since the binary values of “0” and “1” are used, it is assumed that φ(f i )=f i . In this case, Equation (1) corresponds to the operation of weighted sum of the transmission coefficients of the selection model with the analysis information used as a weight. If the scores of interlanguage candidates are calculated according to Equation (1), the following result is obtained.
         Z (English)=0.9+1.0+0.2=2.1   Z (Chinese)=0.7+1.0+0.2=1.9   Z (Korean)=0.8+1.0+0.8=2.6
 
As a result, Korean is selected as the interlanguage.
       

     Next, the translation controller  107  translates an original language sentence by use of the translation device  105 C that translates Japanese into Korean that is the selected interlanguage. 
     In this example, an interlanguage sentence of “   ?” is obtained. “˜ ?” in the interlanguage sentence corresponds to a Japanese sentence of “˜ ? (‘˜desuka?’)” corresponding to the polite expression of “are you?”) and the honorific expression of the original language sentence is naturally maintained in the interlanguage sentence. 
     Then, the translation controller  107  translates the interlanguage sentence by use of the translation device  105 F that translates Korean into German that is the object language. 
     In this example, an object language sentence of “Wie geht&#39;s Ihnen, Hans?” is obtained. Since the interlanguage sentence includes “˜ ?”, the translation result not containing “Wie geht&#39;s dir?”, that is a neutral expression in German, but containing “Wie geht&#39;s Ihnen?”, that corresponds to a more polite expression, can be obtained. 
     As described above, the machine translation apparatus according to this embodiment selects an interlanguage from interlanguage candidates based on the analysis result of the original sentence and the selection model. Therefore, a translation can be executed by use of the interlanguage that is suitable for translation of the input original sentence and, as a result, a highly accurate translation can be executed. 
     Instructions in the processing procedure shown in the above embodiment can be executed based on a program that is software. A general-purpose computer system can provide the same effect as the effect of the machine translation apparatus of the above embodiment by previously storing the program and reading the program. The instructions described in the above embodiment are stored in a magnetic disk (flexible disk, hard disk or the like), optical disk (CD-ROM, CD-R, CD-RW, DUD-ROM, DVD±R, DVD±RW or the like), semiconductor memory, or storage medium corresponding thereto as a program that can be executed by a computer. If a storage medium that can be read by a computer or built-in system is used, any storage form can be used. The computer can achieve the same operation as that of the machine translation apparatus of the above embodiment by reading the program from the storage medium and causing a CPU to execute the instruction described in the program based on the program. Of course, when the computer acquires or reads a program, it is possible to acquire or read the program via a network. 
     Furthermore, an OS (operating system) operated on a computer based on the instruction of the program installed in the computer or built-in system from the storage medium, database management software, MW (middleware) of the network or the like may be used to perform part of each process for realizing the embodiment. 
     Additionally, the storage medium in the present embodiment is not limited to a medium independently provided from the computer or built-in system; it also includes a storage medium that downloads a program transmitted via LAN or network to store or temporarily store the same. 
     The number of storage media is not limited to one; cases where the process in the present embodiment may be realized by a plurality of media and where the media are contained in the storage medium of this embodiment are included, and the configuration of the medium can take any configuration. 
     The computer or built-in system in the present embodiment performs each process in the above embodiment based on the program stored in the storage medium and can be configured by one device formed of a personal computer or microcomputer, a system having devices connected to the network, or the like. 
     The computer in this embodiment indicates not only a personal computer, but also an operation processing device, microcomputer or the like contained in an information processing device and generally indicates an equipment or device that can realize the function of this embodiment based on the program. 
     While certain embodiments have been described, these embodiments have been presented by way of example only, and are not intended to limit the scope of the inventions. Indeed, the novel embodiments described herein may be embodied in a variety of other forms; furthermore, various omissions, substitutions and changes in the form of the embodiments described herein may be made without departing from the spirit of the inventions. The accompanying claims and their equivalents are intended to cover such forms or modifications as would fall within the scope and spirit of the inventions.