NYU-MILA Neural Machine Translation Systems for WMT’16

By Junyoung Chung, Kyunghyun Cho, Yoshua Bengio. 2016

7 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Machine Translation

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Trainable Greedy Decoding for Neural Machine Translation (2017, background): “Besides its success in traditional settings of machine translation, that is one-to-one translation be-tween two languages, (Sennrich et al., 2016; Chung et al., 2016), neural machine translation has ven-tured into more sophisticated settings of machine The majority of this work was completed while…”