Rethink about the Word-level Quality Estimation for Machine Translation from Human Judgement

TL;DR

This paper introduces a human-annotated benchmark dataset for word-level quality estimation in machine translation, addressing limitations of automatic metrics by incorporating expert human judgments and proposing self-supervised strategies to improve artificial data quality.

Contribution

The paper presents HJQE, a human-annotated dataset for word-level QE, and proposes self-supervised tag correction strategies to align artificial QE data with human judgments.

Findings

HJQE dataset aligns better with human judgment than existing metrics.

Self-supervised tag correction strategies improve artificial QE data quality.

Experimental results validate the effectiveness of the proposed methods.

Abstract

Word-level Quality Estimation (QE) of Machine Translation (MT) aims to find out potential translation errors in the translated sentence without reference. Typically, conventional works on word-level QE are designed to predict the translation quality in terms of the post-editing effort, where the word labels ("OK" and "BAD") are automatically generated by comparing words between MT sentences and the post-edited sentences through a Translation Error Rate (TER) toolkit. While the post-editing effort can be used to measure the translation quality to some extent, we find it usually conflicts with the human judgement on whether the word is well or poorly translated. To overcome the limitation, we first create a golden benchmark dataset, namely \emph{HJQE} (Human Judgement on Quality Estimation), where the expert translators directly annotate the poorly translated words on their judgements. Additionally, to further make use of the parallel corpus, we propose the self-supervised pre-training with two tag correcting strategies, namely tag refinement strategy and tree-based annotation strategy, to make the TER-based artificial QE corpus closer to \emph{HJQE}. We conduct substantial experiments based on the publicly available WMT En-De and En-Zh corpora. The results not only show our proposed dataset is more consistent with human judgment but also confirm the effectiveness of the proposed tag correcting strategies.\footnote{The data can be found at \url{https://github.com/ZhenYangIACAS/HJQE}.}