In language modeling, experimental results in (Mikolov et al., 2011;Arisoy et al., 2012;Sundermeyer et al., 2013) showed that recurrent neural networks (RNNs) outperform feed-forward neural networks in both perplexity and word error rate in speech recognition even though it is harder to train properly.
in this paper we take the advantages of RNN and tuple sequence model and propose recurrent neural network-based tuple sequence models (RNNTSMs) to improve phrase-based translation system.