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        <title>Lexical Translation Inconsistency-Aware Document-Level Translation Repair</title>
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-acl.791</identifier>
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%0 Conference Proceedings
%T Lexical Translation Inconsistency-Aware Document-Level Translation Repair
%A Zhang, Zhen
%A Li, Junhui
%A Tao, Shimin
%A Yang, Hao
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F zhang-etal-2023-lexical
%X 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.
%R 10.18653/v1/2023.findings-acl.791
%U https://aclanthology.org/2023.findings-acl.791
%U https://doi.org/10.18653/v1/2023.findings-acl.791
%P 12492-12505

Markdown (Informal)

[Lexical Translation Inconsistency-Aware Document-Level Translation Repair](https://aclanthology.org/2023.findings-acl.791) (Zhang et al., Findings 2023)

• Lexical Translation Inconsistency-Aware Document-Level Translation Repair (Zhang et al., Findings 2023)

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.