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

Anonymous

Keywords: quality estimation, machine translation, human judgement, benchmark dataset

Abstract: Word-level Quality Estimation (QE) of Machine Translation (MT) aims to detect potential translation errors in the translated sentence without reference. Typically, conventional works on word-level QE are designed to predict the quality of translated words in terms of the post-editing effort, where the word labels in the training and evaluation sets， i.e., \texttt{OK} or \texttt{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{For reviewers, the corpora and codes can be found in the attached files.}

Paper Type: long

Research Area: Machine Translation