Secoco: Self-Correcting Encoding for Neural Machine Translation
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Abstract (arXiv)
This paper presents Self-correcting Encoding (Secoco), a framework that effectively deals with input noise 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.
Key facts. Cited by 2 papers in the map (data through 13 Oct 2026). Builds on 25 of its 30 references that are in the map. First posted on arXiv 27 Aug 2021.
Builds on
25 of 30 references.
References · 25
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13 more
- 2019Training on Synthetic Noise Improves Robustness to Natural Noise in Machine Translationcited by 52
- 2018Denoising Neural Machine Translation Training with Trusted Data and Online Data Selectioncited by 43
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- 2021Autocorrect in the Process of Translation -- Multi-task Learning Improves Dialogue Machine Translationcited by 8
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- 2019Exploring the Robustness of NMT Systems to Nonsensical Inputscited by 5
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Citing papers per year: 2023: 1; 2024: 0; 2025: 1.
2 papers.