Aligning More Words with High Precision for Small Bilingual Corpora

Aligning More Words with High Precision for Small Bilingual Corpora Sur-Jin Ker Department of Computer Science National Tsing Hua University Hsinchu, Taiwan, ROC 30043 j schang@cs.nthu.edu.tw Jason J. S. Chang Department of Computer Science National Tsing tlua University Hsinchu, Taiwan, ROC 30043 jschang@cs.nthu.edu.tw Abstract In this paper, we propose an algorithm for aligning words with their translation in a bilingual corpus. Conventional algorithms are based on word-by-word models which require bilingual data with hundreds of thousand sentences for training. By using a word-based approach, less frequent words or words with diverse translations generally do not have statistically significant evidence for confident alignment. Consequently, incomplete or incorrect alignments occur. Our algorithm attempts to handle the problem using class- based rules which are automatic acquired from bilingual materials such as a bilingual corpus or machine readable dictionary. The procedures for acquiring these rules is also described. We found that the algorithm can align over 80% of word pairs while maintaining a comparably high precision rate, even when a small corpus was used in .training. The algorithm also poses the advantage of producing a tagged corpus for word sense disambiguation. 1. Introduction Brown et al. (1990) initiated much of the recent interest in bilingual corpora. They advocated applying a statistical approach to machine translation (SMT). The SMT approach can be understood as a word by word model consisting of two submodels: a language model for generating a source text segment ST and a translation model for translating ST to a target text segment TT. They recommended using an aligned bilingual corpus to estimate the parameters of translation probability, Pr(ST [TT) in the translation model. The resolution of alignment can vat3, from low to high: section, paragraph, sentence, phrase, and word (Gale and Church 1993; Matsumoto et al. 1993). In addition to machine translation, many applications tbr aligped corpora have been proposed, including bilingual lexicography (Gale and Church 199l, Smadja 1992, Dallie, Gaussier and Lange 1994), and word-sense disambiguation (Gale, Church and Yarowsky 1992, Chen and Chang 1994). In the context of statistical machine translation, Brown et al. (1993) presented a series of five models for Pr(ST [TT). The first two models have been used in research on word alignment. Model 1 assumes that Pr(ST[TT) depends only on lexical translation probability t(s I t), i.e., the probability of the i-th word in ST producing the j-th word t in TT as its translation. The pair of words (s, t) is called a connection. Model 2 enhances Model 1 by considering the dependence of Pr(ST ITT) on the distortion probability, d(i l J, 1, m) where I and m are the numbers of words in ST and TT, respectively. Using an EM algorithm for Model 2, Brown et al. (1990) reported the model produced seventeen acceptable translations for twenty-six testing sentences. However, the degree of success in word alignment was not reported. Dagan, Church and Gale (1992) proposed directly aligning words without the preprocessing phase of sentence alignment. Under this proposal, a rough chm'acter-by-character alignment …

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