A Simple and Effective Unified Encoder for Document-Level Machine Translation

Shuming Ma, Dongdong Zhang, Ming Zhou


Abstract
Most of the existing models for document-level machine translation adopt dual-encoder structures. The representation of the source sentences and the document-level contexts are modeled with two separate encoders. Although these models can make use of the document-level contexts, they do not fully model the interaction between the contexts and the source sentences, and can not directly adapt to the recent pre-training models (e.g., BERT) which encodes multiple sentences with a single encoder. In this work, we propose a simple and effective unified encoder that can outperform the baseline models of dual-encoder models in terms of BLEU and METEOR scores. Moreover, the pre-training models can further boost the performance of our proposed model.
Anthology ID:
2020.acl-main.321
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3505–3511
Language:
URL:
https://www.aclweb.org/anthology/2020.acl-main.321
DOI:
10.18653/v1/2020.acl-main.321
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.acl-main.321.pdf
Video:
 http://slideslive.com/38929020