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arXiv · 2209.11378

Extending Word-Level Quality Estimation for Post-Editing Assistance

Yizhen Wei·Takehito Utsuro·Masaaki Nagata
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Abstract

We define a novel concept called extended word alignment in order to improve post-editing assistance efficiency. Based on extended word alignment, we further propose a novel task called refined word-level QE that outputs refined tags and word-level correspondences. Compared to original word-level QE, the new task is able to directly point out editing operations, thus improves efficiency. To extract extended word alignment, we adopt a supervised method based on mBERT. To solve refined word-level QE, we firstly predict original QE tags by training a regression model for sequence tagging based on mBERT and XLM-R. Then, we refine original word tags with extended word alignment. In addition, we extract source-gap correspondences, meanwhile, obtaining gap tags. Experiments on two language pairs show the feasibility of our method and give us inspirations for further improvement.

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Yizhen Wei, Takehito Utsuro, Masaaki Nagata. 2022-09-23. Extending Word-Level Quality Estimation for Post-Editing Assistance. https://arxiv.org/abs/2209.11378

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