This work presents two different translation models using recurrent neural networks. The first one is a word-based approach using word alignments. Second, we present phrase-based translation models that are more consistent with phrasebased decoding. Moreover, we introduce bidirectional recurrent neural models to the problem of machine translation, allowing us to use the full source sentence in our models, which is also of theoretical interest. We demonstrate that our translation models are capable of improving strong baselines already including recurrent neural 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.
REF also used bidirectional LSTM RNN model to improve strong baselines when modeling translation.
Translation Modeling with Bidirectional Recurrent Neural Networks
EMNLP
Computer Science