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    <abstract>In this paper we aim to relieve the issue of lexical translation inconsistency for document-level neural machine translation (NMT) by modeling consistency preference for lexical chains, which consist of repeated words in a source-side document and provide a representation of the lexical consistency structure of the document. Specifically, we first propose lexical-consistency attention to capture consistency context among words in the same lexical chains. Then for each lexical chain we define and learn a consistency-tailored latent variable, which will guide the translation of corresponding sentences to enhance lexical translation consistency. Experimental results on Chinese→English and French→English document-level translation tasks show that our approach not only significantly improves translation performance in BLEU, but also substantially alleviates the problem of the lexical translation inconsistency.</abstract>
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%0 Conference Proceedings
%T Modeling Consistency Preference via Lexical Chains for Document-level Neural Machine Translation
%A Lyu, Xinglin
%A Li, Junhui
%A Tao, Shimin
%A Yang, Hao
%A Qin, Ying
%A Zhang, Min
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F lyu-etal-2022-modeling
%X In this paper we aim to relieve the issue of lexical translation inconsistency for document-level neural machine translation (NMT) by modeling consistency preference for lexical chains, which consist of repeated words in a source-side document and provide a representation of the lexical consistency structure of the document. Specifically, we first propose lexical-consistency attention to capture consistency context among words in the same lexical chains. Then for each lexical chain we define and learn a consistency-tailored latent variable, which will guide the translation of corresponding sentences to enhance lexical translation consistency. Experimental results on Chinese→English and French→English document-level translation tasks show that our approach not only significantly improves translation performance in BLEU, but also substantially alleviates the problem of the lexical translation inconsistency.
%R 10.18653/v1/2022.emnlp-main.424
%U https://aclanthology.org/2022.emnlp-main.424
%U https://doi.org/10.18653/v1/2022.emnlp-main.424
%P 6312-6326

Markdown (Informal)

[Modeling Consistency Preference via Lexical Chains for Document-level Neural Machine Translation](https://aclanthology.org/2022.emnlp-main.424) (Lyu et al., EMNLP 2022)

• Modeling Consistency Preference via Lexical Chains for Document-level Neural Machine Translation (Lyu et al., EMNLP 2022)

ACL

• Xinglin Lyu, Junhui Li, Shimin Tao, Hao Yang, Ying Qin, and Min Zhang. 2022. Modeling Consistency Preference via Lexical Chains for Document-level Neural Machine Translation. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 6312–6326, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.