Paper
A Character-Level Decoder without Explicit Segmentation for Neural\n Machine Translation
The existing machine translation systems, whether phrase-based or neural,\nhave relied almost exclusively on word-level modelling with explicit\nsegmentation. In this paper, we ask a fundamental question: can neural machine\ntranslation generate a character sequence without any explicit segmentation? To\nanswer this question, we evaluate an attention-based encoder-decoder with a\nsubword-level encoder and a character-level decoder on four language\npairs--En-Cs, En-De, En-Ru and En-Fi-- using the parallel corpora from WMT'15.\nOur experiments show that the models with a character-level decoder outperform\nthe ones with a subword-level decoder on all of the four language pairs.\nFurthermore, the ensembles of neural models with a character-level decoder\noutperform the state-of-the-art non-neural machine translation systems on\nEn-Cs, En-De and En-Fi and perform comparably on En-Ru.\n
Authors: Chung, Junyoung · Cho, Kyunghyun · Bengio, Yoshua