Improved Data Augmentation for Translation Suggestion
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Abstract (arXiv)
Translation suggestion (TS) models are used to automatically provide alternative suggestions for incorrect spans in sentences generated by machine translation. This paper introduces the system used in our submission to the WMT'22 Translation Suggestion shared task. Our system is based on the ensemble of different translation architectures, including Transformer, SA-Transformer, and DynamicConv. We use three strategies to construct synthetic data from parallel corpora to compensate for the lack of supervised data. In addition, we introduce a multi-phase pre-training strategy, adding an additional pre-training phase with in-domain data. We rank second and third on the English-German and English-Chinese bidirectional tasks, respectively.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 7 of its 12 references that are in the map. First posted on arXiv 12 Oct 2022.
Builds on
7 of 12 references.
References · 7
- 2017Attention Is All You Needcited by 59,932
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2019fairseq: A Fast, Extensible Toolkit for Sequence Modelingcited by 1,486
- 2019Pay Less Attention with Lightweight and Dynamic Convolutionscited by 211
- 2021IntelliCAT: Intelligent Machine Translation Post-Editing with Quality Estimation and Translation Suggestioncited by 11
- 2021WeTS: A Benchmark for Translation Suggestioncited by 8
- 2022Cross-Align: Modeling Deep Cross-lingual Interactions for Word Alignmentcited by 2
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Citing papers per year: 2022: 1.
1 paper.