Alignment by Bilingual Generation and Monolingual Derivation

要旨

One of the main issues in a word alignment task is the difficulty of handling function words that do not have direct translations which we call unique function words. They are often aligned to some words in the other language incorrectly. This is prominent in language pairs with very different sentence structures. In this paper, we propose a novel approach for handling unique function words. The proposed model monolingually derives unique function words from bilin-gually generated treelet pairs. The monolingual derivation prevents incorrect alignments for unique function words. The derivation probabilities are estimated from a large monolingual corpus, which is much easier to acquire than a parallel corpus. Also, the proposed alignment model uses semantic-head dependency trees where dependency relations between words be-come similar in each language. Experimental results on an English-Japanese corpus show that the proposed model achieves better alignment and translation quality compared with the baseline models.

主題