CTM: An Example-Based Translation Aid System

This paper describes a Japanese-English translat ion aid system, C'I 'M, which has a usefid capabili ty for flexible retrieval of texts f rom hilingaal corpora or t ranslat ion databases. Transla t ion examples (pairs of a text and its translat ion equivalent) are very helpful for us to t ranslate the similar text. Our character-based best ma tch retrieval method can re: trieve translat ion examples similar to the given input. This me thod has the following advantages: (1) this method accepts free-style t ranslat ion examples, i.e., pairs of any text str ing and its t ranslat ion equivalent, (2) morphological analysis is unnecessary, (3) this method accepts fl'ce-Myle inlmts (i.e., any text strings) for retrieval. We show the retrieval examples with the following characterist ic features: phrasal expression, long-distance dependency, idiom, synonym, mad semant ic ambiguity. 1 I n t r o d u c t i o n In the late 1980's, several commercial JapaneseEnglish machine translat ion systems had heen developed in Japan , In these systems, the computer is the agent of translation, while the user assists in editing the translat ion inputs and revising the results. Although they are usefal to t rans la te large amounts of texts roughly and rapidly, high quality t ranslat ion is impossible. T r a n s l a t i o n a M is another kind of machine translation: the user is the agent of t ranslat ion, while the compute r provides him or her with the helpfifl tools, e.g., quick-retrieval electronic dictionaries. A quickretrieval bilingual corpus is also usefifl, specifically when it h,'~s the flexihle (best match) retrieval mechanism. Because translat iml examples (pairs of source text, and its t ranslat ion equivalent) are very helpful for us to t ranslate the similar text. This type of system is called a~ example-based translalion aid [6], and there are two prototype systems in Japanese-English translation: E T O C [8] and Nakanmra ' s sys tem [5]. *The author had been I, rallsfetTeiI from Kyot(~ I.Jllivenlity ()n April I, 1992. This work w~. ~ done al Kyolo University. [Intmt Text] (He is a great swimmer.) t Best Match ~ Retrieval J [Retrieved EXaml)h, ] YOll~re ~z great actor. Figure h B~ ic Configuration of Example-B,'Lsed Translation Aid Figure I shows the basic configuration of exampleha~ed translation aid (EI~TA). I t consists of two components: the t r a n s l a t i o n d a t a b a s e is the collection of t ranslat ion examples, and the b e s t m a t c h ret r i e v a l engine is to retrieve the example tha t is the most similar to the given input text. The characteristic of the EBTA sys tem is tha t it accepts free-style texl inputs for the retrieval: it frees the user f rom learning the tbrmal language for datah,~se query. 'l 'he central problem in EBTA is the implementation of the hest ma tch retrieval. Two methods were proposed: one is the syn tax-match ing driven by generalization rules in E T O C [8], and the o ther is Nakanmra ' s method using content words [5]. They are the w o r d b a s e d b e s t m a t c h r e t r i e v a l me thods 1, which need morphological analysis. This paper proposes the c h a r a c t e r b a s e d b e s t m a t c h r e t r i e v a l me thod , specifically for Japanese t e x t s Compared with the word-hased methods, the charaeter-h~sed method has the following advantages: • Morphological analysis is unnecessary. • Some kind of synonyms can he retr ieved without a thesallrlls. This method has been implemented in CTM ~, a Japanese-English translat ion aid system for writing / t rans la t ing technical papers. 1 ]n w.rd bm~ed (reap. char~'ter b&sed) hem lll/~tc|t retrieval method, a word (Ires n. chara~'ter) is a primitive. 2C'I'M is named frc~m th~ . lalmnese p h r ~ , "Chotto 'Isukatte Mitene", which means "nNe it naly time you want". A c r ~ DE COLING-92, NANTES. 23-28 AO(H" 1992 I 2 5 9 Prec. o~: COLING-92. NAI~rrEs, Auo. 23-28, 1992 2 T h e C h a r a c t e r B a s e d B e s t M a t c h R e t r i e v a l M e t h o d 2.1 C h a r a c t e r i s t i c s o f J a p a n e s e W r i t t e n T e x t s Japanese wri t ten texts have remarkable characteristics as follows. 'riley cannot he found in European languages, i.e., English, French, and German . 1. The number of characters is very large. The numher of characters that are used ill text is more than 7,000 in Japanese. while it is less than a hundred in a European language. 2. Synouyms often have the same Kanji character . Japanese characters are divided into three types'. Hiragana (83 characters) , Ka t akana (86 characters) , and Kanji. A l t i ragana or Ka takana character expresses a s o u n d , and a Kanji character represents a semantic primitive. For example, tile Kanji character " ~ " means "thinking", and it is used for constructlug several words concerned with thinking: e.g., ,~( ~( th inking)~ ~",~, (consideration), ~ ,~ ' ( deep thinking), ~ ~ T a (think), ~ [ ~ T T a (devise). 3. There is no delimiter between words. In l"uropean languages, the white space is the delimiter for word separation. In contrast , Japanese has no explicit delimiter. Therefore, the main par t of Japanese morphological analysis is to divide a text str ing into words: it is not easy task a. These characteristics of Japanese suggest the c h a r a c t e r b a s e d b e s t l l la te | l , becanse I While the word-based method needs morphological analysis, the character-bmsed method does not need it. 2. In order to retrieve synonyms the word-based method needs a thesaurus . In coutra.st, the character-based method call retrieve some kind of synonyms withont a thesallrus, because synonynls often have tile same Kanji character in Japanese. 2.2 T h e Character-Based Best Match The character-based best match can be determined by defining the distance or similarity measure between two strings. The simple measure of similarity hetween two strings, A = alau. a~. , H = b tb2 . by , is the number of the match ing characters considering the character order constraint . It is not part icularly good a F o r e x a m p l e , a Jap&llese morphoh~g ica l anMyMs pFOgl'&lll developed by Nyolt~ University fails to anMyze 3 ~ ,5 % of Selll ellCeS. memsure, ba t makes a convenient s tar t ing point. We define it as follows: s ( i , j ) = 0 i f i = o v j = o s ( i l , j l ) + m ( i , j ) , ) max s(i l , j ) , s ( i , j 1) i f ( l _< i < x) A(1 < _ j < y ) 1 if a~ = b 3 m { i , j ) = 0 i f a , ~ b j This measure often produces the undesirable resuits, because we ignore continuation of match ing characters. For example, consider the following strings: A = I " I ~ R 4 ~ ' ¢ 7 0 (solve the problem) f~ = t ~ a ~ m 5 , ~ j ~ 1 c e ~ Ltco (He solved the problem yesterday.) (de termine tile method for solving the problem) We want to be S(A,13) > ,9(A, F¢'), but the above measure produces ,5'(A, B) < ,S'(A, B~). To solve the problem, we consider tile bonus for cont immns matching characters. It can be done by modifying m ( i , j ) m the the above definition: ,5'(A,,~) = s(x, : j )