Context-aware Decoder for Neural Machine Translation using a Target-side Document-Level Language Model

Amane Sugiyama, Naoki Yoshinaga


Abstract
Although many end-to-end context-aware neural machine translation models have been proposed to incorporate inter-sentential contexts in translation, these models can be trained only in domains where parallel documents with sentential alignments exist. We therefore present a simple method to perform context-aware decoding with any pre-trained sentence-level translation model by using a document-level language model. Our context-aware decoder is built upon sentence-level parallel data and target-side document-level monolingual data. From a theoretical viewpoint, our core contribution is the novel representation of contextual information using point-wise mutual information between context and the current sentence. We demonstrate the effectiveness of our method on English to Russian translation, by evaluating with BLEU and contrastive tests for context-aware translation.
Anthology ID:
2021.naacl-main.461
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
June
Year:
2021
Address:
Online
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5781–5791
Language:
URL:
https://aclanthology.org/2021.naacl-main.461
DOI:
10.18653/v1/2021.naacl-main.461
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.naacl-main.461.pdf