Fully Character-Level Neural Machine Translation without Explicit Segmentation

Jason Lee, Kyunghyun Cho, Thomas Hofmann

[How to correct problems with metadata yourself]


Abstract
Most existing machine translation systems operate at the level of words, relying on explicit segmentation to extract tokens. We introduce a neural machine translation (NMT) model that maps a source character sequence to a target character sequence without any segmentation. We employ a character-level convolutional network with max-pooling at the encoder to reduce the length of source representation, allowing the model to be trained at a speed comparable to subword-level models while capturing local regularities. Our character-to-character model outperforms a recently proposed baseline with a subword-level encoder on WMT’15 DE-EN and CS-EN, and gives comparable performance on FI-EN and RU-EN. We then demonstrate that it is possible to share a single character-level encoder across multiple languages by training a model on a many-to-one translation task. In this multilingual setting, the character-level encoder significantly outperforms the subword-level encoder on all the language pairs. We observe that on CS-EN, FI-EN and RU-EN, the quality of the multilingual character-level translation even surpasses the models specifically trained on that language pair alone, both in terms of the BLEU score and human judgment.
Anthology ID:
Q17-1026
Volume:
Transactions of the Association for Computational Linguistics, Volume 5
Month:
Year:
2017
Address:
Cambridge, MA
Editors:
Lillian Lee, Mark Johnson, Kristina Toutanova
Venue:
TACL
SIG:
Publisher:
MIT Press
Note:
Pages:
365–378
Language:
URL:
https://aclanthology.org/Q17-1026
DOI:
10.1162/tacl_a_00067
Bibkey:
Cite (ACL):
Jason Lee, Kyunghyun Cho, and Thomas Hofmann. 2017. Fully Character-Level Neural Machine Translation without Explicit Segmentation. Transactions of the Association for Computational Linguistics, 5:365–378.
Cite (Informal):
Fully Character-Level Neural Machine Translation without Explicit Segmentation (Lee et al., TACL 2017)
Copy Citation:
PDF:
https://preview.aclanthology.org/teach-a-man-to-fish/Q17-1026.pdf
Video:
 https://preview.aclanthology.org/teach-a-man-to-fish/Q17-1026.mp4
Code
 nyu-dl/dl4mt-c2c +  additional community code