Study of Practical Effectiveness for Machine Translation Using Recursive Chain-link-type Learning

Study of Practical Effectiveness for Machine Translation Using Recursive Chain-link-type Learning Hiroshi Echizen-ya Dept. of Electronics and Information Hokkai-Gakuen University S 26-Jo, W 11-Chome, Chuo-ku Sapporo, 064-0926 Japan echi@eli.hokkai-s-u.ac.jp Kenji Araki Division of Electronics and Information Hokkaido University N 13-Jo, W 8-Chome, Kita-ku Sapporo, 060-8628 Japan araki@media.eng.hokudai.ac.jp Yoshio Momouchi Dept. of Electronics and Information Hokkai-Gakuen University S 26-Jo, W 11-Chome, Chuo-ku Sapporo, 064-0926 Japan momouchi@eli.hokkai-s-u.ac.jp Koji Tochinai Division of Business Administration Hokkai-Gakuen University 4-Chome, Asahi-machi, Toyohira-ku Sapporo, 060-8790 Japan tochinai@econ.hokkai-s-u.ac.jp Abstract A number of machine translation systems based on the learning algorithms are presented. These methods acquire translation rules from pairs of similar sentences in a bilingual text cor- pora. This means that it is difficult for the systems to acquire the translation rules from sparse data. As a result, these methods require large amounts of training data in order to ac- quire high-quality translation rules. To over- come this problem, we propose a method of ma- chine translation using a Recursive Chain-link- type Learning. In our new method, the system can acquire many new high-quality translation rules from sparse translation examples based on already acquired translation rules. Therefore, acquisition of new translation rules results in the generation of more new translation rules. Such a process of acquisition of translation rules is like a linked chain. From the results of evalua- tion experiments, we confirmed the effectiveness of Recursive Chain-link-type Learning. 1 Introduction Rule-Based Machine Translation(MT)(Hutchins and Somers, 1992) requires large-scale knowl- edge to analyze both source language(SL) sentences and target language(TL) sentences. Moreover, it is difficult for a developer to com- pletely describe large-scale knowledge that can analyze various linguistic phenomena. There- fore, Rule-Based MT is time-consuming and expensive. Statistical MT and Example-Based MT have been proposed to overcome the dif- ficulties of Rule-Based MT. These approaches correspond to Corpus-Based approach. Corpus- Based approach uses translation examples that keep including linguistic knowledge. This means that the system can improve the quality of its translation only by adding new translation examples. However, in Statistical MT(Brown et al., 1990), large amounts of translation examples are required in order to obtain high-quality translation. Moreover, Example- Based MT(Sato and Nagao, 1990; Watanabe and Takeda, 1998; Brown, 2001; Carl, 2001) which relies on various knowledge resources results in the same difficulties as Rule-Based MT. Therefore, Example-Based MT, which automatically acquires the translation rules from only bilingual text corpora, is very effec- tive. However, existing Example-Based MT systems using the learning algorithms require large amounts of translation pairs to acquire high-quality translation rules. In Example-Based MT based on analogical reasoning(Malavazos, 2000; Guvenir, 1998), the different parts are replaced by variables to gen- eralize translation examples as shown in (1) of Figure 1. However, the number of different parts of the two SL sentences must be same as the number of different parts of the two TL sentences. This means that the condition of ac- quisition of translation rules is very strict be- cause this method allows only n:n mappings in the number of the different …

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