Multi-Source Syntactic Neural Machine Translation

Anna Currey, Kenneth Heafield


Abstract
We introduce a novel multi-source technique for incorporating source syntax into neural machine translation using linearized parses. This is achieved by employing separate encoders for the sequential and parsed versions of the same source sentence; the resulting representations are then combined using a hierarchical attention mechanism. The proposed model improves over both seq2seq and parsed baselines by over 1 BLEU on the WMT17 English-German task. Further analysis shows that our multi-source syntactic model is able to translate successfully without any parsed input, unlike standard parsed methods. In addition, performance does not deteriorate as much on long sentences as for the baselines.
Anthology ID:
D18-1327
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Editors:
Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
2961–2966
Language:
URL:
https://aclanthology.org/D18-1327
DOI:
10.18653/v1/D18-1327
Bibkey:
Cite (ACL):
Anna Currey and Kenneth Heafield. 2018. Multi-Source Syntactic Neural Machine Translation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2961–2966, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Multi-Source Syntactic Neural Machine Translation (Currey & Heafield, EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/D18-1327.pdf