Building a Bilingual Lexicon Using Phrase-based Statistical Machine Translation via a Pivot Language

This paper proposes a novel method for building a bilingual lexicon through a pivot language by using phrase-based statistical machine translation (SMT). Given two bilingual lexicons between language pairs Lf–Lp and Lp–Le, we assume these lexicons as parallel corpora. Then, we merge the extracted two phrase tables into one phrase table between Lf and Le. Finally, we construct a phrase-based SMT system for translating the terms in the lexicon Lf–Lp into terms of Le and, obtain a new lexicon Lf–Le. In our experiments with Chinese-English and JapaneseEnglish lexicons, our system could cover 72.8% of Chinese terms and drastically improve the utilization ratio.