On the Properties of Neural Machine Translation: Encoder-Decoder Approaches

On the Properties of Neural Machine Translation: Encoder-Decoder Approaches Kyunghyun Cho Bart van Merri enboer Universit e de Montr eal Dzmitry Bahdanau Jacobs University, Germany Yoshua Bengio Universit e de Montr eal, CIFAR Senior Fellow Abstract Neural machine translation is a relatively new approach to statistical machine translation based purely on neural networks. The neural machine translation models often consist of an encoder and a decoder. The encoder extracts a fixed-length representation from a variable-length input sentence, and the decoder generates a correct translation from this representation. In this paper, we focus on analyzing the properties of the neural machine translation using two models; RNN Encoder-Decoder and a newly proposed gated recursive con-volutional neural network. We show that the neural machine translation performs relatively well on short sentences without unknown words, but its performance degrades rapidly as the length of the sentence and the number of unknown words increase. Furthermore, we find that the proposed gated recursive convolutional network learns a grammatical structure of a sentence automatically. 1 Introduction A new approach for statistical machine translation based purely on neural networks has recently been proposed (Kalchbrenner and Blunsom, 2013; Sutskever et al., 2014). This new approach, which we refer to as neural machine translation, is inspired by the recent trend of deep representational learning. All the neural network models used in (Kalchbrenner and Blunsom, 2013; Sutskever et al., 2014; Cho et al., 2014) consist of an encoder and a decoder.