Year: 2022
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Wei, Yizhen, Utsuro, Takehito, Nagata, Masaaki
Institutions: NTT (Japan), University of Tsukuba
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4297166097