Paper: Recurrent Neural Network-based Tuple Sequence Model for Machine Translation

ACL ID C14-1180
Title Recurrent Neural Network-based Tuple Sequence Model for Machine Translation
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2014
Authors

In this paper, we propose a recurrent neural network-based tuple sequence model (RNNTSM) that can help phrase-based translation model overcome the phrasal independence assumption. Our RNNTSM can potentially capture arbitrary long contextual information during estimating probabilities of tuples in continuous space. It, however, has severe data sparsity problem due to the large tuple vocabulary coupled with the limited bilingual training data. To tackle this problem, we propose two improvements. The first is to factorize bilingual tuples of RNNTSM into source and target sides, we call factorized RNNTSM. The second is to decompose phrasal bilingual tuples to word bilingual tuples for providing fine-grained tuple model. Our extensive experimental results on the IWSLT2012 test sets 1 showed tha...