# Improved Data Augmentation for Translation Suggestion

arXiv: https://arxiv.org/abs/2210.06138 · HTML: https://mlsanity.com/paper/2210.06138/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 12 references that are in the map.
- First posted on arXiv 12 Oct 2022.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [Pay Less Attention with Lightweight and Dynamic Convolutions](https://mlsanity.com/paper/1901.10430/) (2019)
- [IntelliCAT: Intelligent Machine Translation Post-Editing with Quality Estimation and Translation Suggestion](https://mlsanity.com/paper/2105.12172/) (2021)
- [WeTS: A Benchmark for Translation Suggestion](https://mlsanity.com/paper/2110.05151/) (2021)
- [Cross-Align: Modeling Deep Cross-lingual Interactions for Word Alignment](https://mlsanity.com/paper/2210.04141/) (2022)

## Built on by (most cited first, 1 of 1)

- [Findings of the WMT 2022 Shared Task on Translation Suggestion](https://mlsanity.com/paper/2211.16717/) (2022)

## 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.