Abstract
Following the idea of “one translation per discourse”, in this paper we aim to improve translation consistency via document-level translation repair (DocRepair), i.e., automatic post-editing on translations of documents. To this end, we propose a lexical translation inconsistency-aware DocRepair to explicitly model translation inconsistency. First we locate the inconsistency in automatic translation. Then we provide translation candidates for those inconsistency. Finally, we propose lattice-like input to properly model inconsistent tokens and phrases and their candidates. Experimental results on three document-level translation datasets show that based on G-Transformer, a state-of-the-art document-to-document (Doc2Doc) translation model, our Doc2Doc DocRepair achieves significant improvement on translation quality in BLEU scores, but also greatly improves lexical translation consistency.- Anthology ID:
- 2023.findings-acl.791
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2023
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 12492–12505
- Language:
- URL:
- https://aclanthology.org/2023.findings-acl.791
- DOI:
- 10.18653/v1/2023.findings-acl.791
- Cite (ACL):
- Zhen Zhang, Junhui Li, Shimin Tao, and Hao Yang. 2023. Lexical Translation Inconsistency-Aware Document-Level Translation Repair. In Findings of the Association for Computational Linguistics: ACL 2023, pages 12492–12505, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Lexical Translation Inconsistency-Aware Document-Level Translation Repair (Zhang et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2023.findings-acl.791.pdf