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The JHU-Microsoft Submission for WMT21 Quality Estimation Shared Task

2021-09-17 19:13:31
Shuoyang Ding, Marcin Junczys-Dowmunt, Matt Post, Christian Federmann, Philipp Koehn
arXiv_CL
   
Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/2109.08724

PDF

https://arxiv.org/pdf/2109.08724.pdf

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