Paper
NYU-MILA Neural Machine Translation Systems for WMT’16
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.
Authors: Junyoung Chung · Kyunghyun Cho · Yoshua Bengio