Paper: Translation Modeling with Bidirectional Recurrent Neural Networks

ACL ID D14-1003
Title Translation Modeling with Bidirectional Recurrent Neural Networks
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2014
Authors

This work presents two different trans- lation models using recurrent neural net- works. The first one is a word-based ap- proach using word alignments. Second, we present phrase-based translation mod- els that are more consistent with phrase- based decoding. Moreover, we introduce bidirectional recurrent neural models to the problem of machine translation, allow- ing us to use the full source sentence in our models, which is also of theoretical inter- est. We demonstrate that our translation models are capable of improving strong baselines already including recurrent neu- ral language models on three tasks: IWSLT 2013 German?English, BOLT Arabic?English and Chinese?English. We obtain gains up to 1.6% BLEU and 1.7% TER by rescoring 1000-best lists.