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
- Editors:
- Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tur, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, Yichao Zhou
- 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
- Cite (ACL):
- Amane Sugiyama and Naoki Yoshinaga. 2021. Context-aware Decoder for Neural Machine Translation using a Target-side Document-Level Language Model. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 5781–5791, Online. Association for Computational Linguistics.
- Cite (Informal):
- Context-aware Decoder for Neural Machine Translation using a Target-side Document-Level Language Model (Sugiyama & Yoshinaga, NAACL 2021)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2021.naacl-main.461.pdf