Chunk-Based Statistical Translation

Chunk-based Statistical Translation Taro Watanabe†, Eiichiro Sumita† and Hiroshi G. Okuno‡ {taro.watanabe, eiichiro.sumita}@atr.co.jp † ATR Spoken Language Translation ‡Department of Intelligence Science Research Laboratories and Technology 2-2-2 Hikaridai, Keihanna Science City Graduate School of Informatics, Kyoto Uniersity Kyoto 619-0288 JAPAN Kyoto 606-8501 JAPAN Abstract This paper describes an alternative trans- lation model based on a text chunk un- der the framework of statistical machine translation. The translation model sug- gested here first performs chunking. Then, each word in a chunk is translated. Fi- nally, translated chunks are reordered. Under this scenario of translation model- ing, we have experimented on a broad- coverage Japanese-English traveling cor- pus and achieved improved performance. 1 Introduction The framework of statistical machine translation for- mulates the problem of translating a source sentence in a language J into a target language E as the maximization problem of the conditional probability ˆE = argmaxE P(E|J). The application of the Bayes Rule resulted in ˆE = argmaxE P(E)P(J|E). The for- mer term P(E) is called a language model, repre- senting the likelihood of E. The latter term P(J|E) is called a translation model, representing the gener- ation probability from E into J. As an implementation of P(J|E), the word align- ment based statistical translation (Brown et al., 1993) has been successfully applied to similar lan- guage pairs, such as French–English and German– English, but not to drastically different ones, such as Japanese–English. This failure has been due to the limited representation by word alignment and the weak model structure for handling complicated word correspondence. This paper provides a chunk-based statistical translation as an alternative to the word alignment based statistical translation. The translation process inside the translation model is structured as follows. A source sentence is first chunked, and then each chunk is translated into target language with local word alignments. Next, translated chunks are re- ordered to match the target language constraints. Based on this scenario, the chunk-based statis- tical translation model is structured with several components and trained by a variation of the EM- algorithm. A translation experiment was carried out with a decoder based on the left-to-right beam search. It was observed that the translation quality improved from 46.5% to 52.1% in BLEU score and from 59.2% to 65.1% in subjective evaluation. The next section briefly reviews the word align- ment based statistical machine translation (Brown et al., 1993). Section 3 discusses an alternative ap- proach, a chunk-based translation model, ranging from its structure to training procedure and decod- ing algorithm. Then, Section 4 provides experimen- tal results on Japanese-to-English translation in the traveling domain, followed by discussion. 2 Word Alignment Based Statistical Translation Word alignment based statistical translation rep- resents bilingual correspondence by the notion of word alignment A, allowing one-to-many generation from each source word. Figure 1 illustrates an exam- ple of English and Japanese sentences, E and J, with sample word alignments. In this example, “show1” has generated two words, “mise5” and “tekudasai6”. E = NULL0 show1 me2 the3 one4 in5 the6 …

Content truncated to 500 words