# The JHU-Microsoft Submission for WMT21 Quality Estimation Shared Task

arXiv: https://arxiv.org/abs/2109.08724 · HTML: https://mlsanity.com/paper/2109.08724/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 21 references that are in the map.
- First posted on arXiv 17 Sep 2021.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [Beyond English-Centric Multilingual Machine Translation](https://mlsanity.com/paper/2010.11125/) (2020)
- [Levenshtein Transformer](https://mlsanity.com/paper/1905.11006/) (2019)
- [Facebook FAIR's WMT19 News Translation Task Submission](https://mlsanity.com/paper/1907.06616/) (2019)
- [OpenKiwi: An Open Source Framework for Quality Estimation](https://mlsanity.com/paper/1902.08646/) (2019)
- [IntelliCAT: Intelligent Machine Translation Post-Editing with Quality Estimation and Translation Suggestion](https://mlsanity.com/paper/2105.12172/) (2021)
- [Improving Non-autoregressive Neural Machine Translation with Monolingual Data](https://mlsanity.com/paper/2005.00932/) (2020)
- [Levenshtein Training for Word-level Quality Estimation](https://mlsanity.com/paper/2109.05611/) (2021)

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

- [QUAK: A Synthetic Quality Estimation Dataset for Korean-English Neural Machine Translation](https://mlsanity.com/paper/2209.15285/) (2022)

## Abstract (arXiv)

This paper presents the JHU-Microsoft joint submission for WMT 2021 quality estimation shared task. We only participate in Task 2 (post-editing effort estimation) of the shared task, focusing on the target-side word-level quality estimation. The techniques we experimented with include Levenshtein Transformer training and data augmentation with a combination of forward, backward, round-trip translation, and pseudo post-editing of the MT output. We demonstrate the competitiveness of our system compared to the widely adopted OpenKiwi-XLM baseline. Our system is also the top-ranking system on the MT MCC metric for the English-German language pair.