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Improved Data Augmentation for Translation Suggestion

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arxiv 2210.06138 v1 pith:STP3D3OG submitted 2022-10-12 cs.CL

Improved Data Augmentation for Translation Suggestion

Hongxiao Zhang , Siyu Lai , Songming Zhang , Hui Huang , Yufeng Chen , Jinan Xu , Jian Liu This is my paper
classification cs.CL
keywords translationdatasuggestionpre-trainingsystemusedaddingaddition
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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.

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