Agreement-based Learning of Parallel Lexicons and Phrases from Non-Parallel Corpora

Chunyang Liu, Yang Liu, Maosong Sun, Huan-Bo Luan, Heng Yu

Abstract: We introduce an agreement-based approach to learning parallel lexicons and phrases from non-parallel corpora. The basic idea is to encourage two asymmetric latent-variable translation models (i.e., source-to-target and target-to-source) to agree on identifying latent phrase and word alignments. The agreement is defined at both word and phrase levels. We develop a Viterbi EM algorithm for jointly training the two unidirectional models efficiently. Experiments on the ChineseEnglish dataset show that agreementbased learning significantly improves both alignment and translation performance.