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

06/15/2016
by   Chunyang Liu, et al.
0

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 Chinese-English dataset show that agreement-based learning significantly improves both alignment and translation performance.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset