一句话：针对机器翻译系统词义消歧能力，提出基于反向演绎的测试方法，填补相关测试空白。
本文关注机器翻译系统词义消歧（WSD）能力的测试问题。机器翻译系统每天为大量用户提供跨语言服务，正确判断多义词在给定上下文中的具体词义是其核心功能需求之一。然而，Google Translate 等商业系统已被发现会识别错误词义，进而导致翻译错误。令人意外的是，此前没有研究专门针对机器翻译系统的 WSD bug 进行测试。为此，论文提出基于反向演绎的测试方法（back deduction based testing），旨在系统性地暴露此类词义消歧缺陷。摘要未报告具体实验评估与结果，主要贡献在于指出该测试缺口并提出相应测试思路。
Machine translation systems have penetrated our daily lives, providing translation services from source language to target language to millions of users online daily. Word Sense Disambiguation (WSD) is one of the essential functional requirements of machine translation systems, which aims to determine the exact sense of polysemes in the given context. Commercial machine translation systems (e.g., Google Translate) have been shown to fail in identifying the proper sense and consequently cause translation errors. However, to our knowledge, no prior studies focus on testing such WSD bugs for machine translation systems.
@inproceedings{paperbot8052,
title = {Back Deduction Based Testing for Word Sense Disambiguation Ability of Machine Translation Systems},
author = {Jun Wang and Yanhui Li and Xiang Huang and Lin Chen and Xiaofang Zhang and Yuming Zhou},
booktitle = {ISSTA},
year = {2023},
doi = {10.1145/3597926.3598081}
}