NYU-MILA Neural Machine Translation Systems for WMT’16
Jun‐Young Chung, Kyunghyun Cho, Yoshua Bengio · 2016
We describe the neural machine translation system of New York University (NYU) and University of Montreal (MILA) for the translation tasks of WMT'16.The main goal of NYU-MILA submission to WMT'16 is to evaluate a new character-level decoding approach in neural machine translation on various language pairs.The proposed neural machine translation system is an attention-based encoder-decoder with a subword-level encoder and a character-level decoder.The decoder of the neural machine translation system does not require explicit segmentation, when characters are used as tokens.The character-level decoding approach provides benefits especially when translating a source language into other morphologically rich languages.