DeepAI AI Chat
Log In Sign Up

A Character-Level Decoder without Explicit Segmentation for Neural Machine Translation

by   Junyoung Chung, et al.
Université de Montréal

The existing machine translation systems, whether phrase-based or neural, have relied almost exclusively on word-level modelling with explicit segmentation. In this paper, we ask a fundamental question: can neural machine translation generate a character sequence without any explicit segmentation? To answer this question, we evaluate an attention-based encoder-decoder with a subword-level encoder and a character-level decoder on four language pairs--En-Cs, En-De, En-Ru and En-Fi-- using the parallel corpora from WMT'15. Our experiments show that the models with a character-level decoder outperform the ones with a subword-level decoder on all of the four language pairs. Furthermore, the ensembles of neural models with a character-level decoder outperform the state-of-the-art non-neural machine translation systems on En-Cs, En-De and En-Fi and perform comparably on En-Ru.


Fully Character-Level Neural Machine Translation without Explicit Segmentation

Most existing machine translation systems operate at the level of words,...

Character-Aware Decoder for Neural Machine Translation

Standard neural machine translation (NMT) systems operate primarily on w...

Towards Neural Machine Translation with Latent Tree Attention

Building models that take advantage of the hierarchical structure of lan...

Neural Machine Translation with Characters and Hierarchical Encoding

Most existing Neural Machine Translation models use groups of characters...

Plan, Attend, Generate: Character-level Neural Machine Translation with Planning in the Decoder

We investigate the integration of a planning mechanism into an encoder-d...

Approaching Neural Chinese Word Segmentation as a Low-Resource Machine Translation Task

Supervised Chinese word segmentation has been widely approached as seque...

Code Repositories