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 showed that the proposed approach essentially improved the translation quality over state-of-the-art phrase-based translation systems (baselines) and recurrent neural network language models (RNNLMs). Compared with the baselines, the BLEU scores on English-French and English-German tasks were greatly enhanced by 2.1%-2.6% and 1.8%-2.1%, respectively.
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