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    <abstract>This paper presents Self-correcting Encoding (Secoco), a framework that effectively deals with noisy input for robust neural machine translation by introducing self-correcting predictors. Different from previous robust approaches, Secoco enables NMT to explicitly correct noisy inputs and delete specific errors simultaneously with the translation decoding process. Secoco is able to achieve significant improvements over strong baselines on two real-world test sets and a benchmark WMT dataset with good interpretability. We will make our code and dataset publicly available soon.</abstract>
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%0 Conference Proceedings
%T Secoco: Self-Correcting Encoding for Neural Machine Translation
%A Wang, Tao
%A Zhao, Chengqi
%A Wang, Mingxuan
%A Li, Lei
%A Li, Hang
%A Xiong, Deyi
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F wang-etal-2021-secoco-self
%X This paper presents Self-correcting Encoding (Secoco), a framework that effectively deals with noisy input for robust neural machine translation by introducing self-correcting predictors. Different from previous robust approaches, Secoco enables NMT to explicitly correct noisy inputs and delete specific errors simultaneously with the translation decoding process. Secoco is able to achieve significant improvements over strong baselines on two real-world test sets and a benchmark WMT dataset with good interpretability. We will make our code and dataset publicly available soon.
%R 10.18653/v1/2021.findings-emnlp.396
%U https://aclanthology.org/2021.findings-emnlp.396
%U https://doi.org/10.18653/v1/2021.findings-emnlp.396
%P 4639-4644

Markdown (Informal)

[Secoco: Self-Correcting Encoding for Neural Machine Translation](https://aclanthology.org/2021.findings-emnlp.396) (Wang et al., Findings 2021)

• Secoco: Self-Correcting Encoding for Neural Machine Translation (Wang et al., Findings 2021)

ACL

• Tao Wang, Chengqi Zhao, Mingxuan Wang, Lei Li, Hang Li, and Deyi Xiong. 2021. Secoco: Self-Correcting Encoding for Neural Machine Translation. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 4639–4644, Punta Cana, Dominican Republic. Association for Computational Linguistics.