Back Deduction Based Testing for Word Sense Disambiguation Ability of Machine Translation Systems
Machine translation systems have penetrated our daily lives, providing translation services from source language to target language to millions of users online daily. \textbf{W}ord \textbf{S}ense \textbf{D}isambiguation (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.
To tackle this challenge, we propose a novel testing method \textbf{B}ack \textbf{D}eduction based \textbf{T}esting for Word Sense \textbf{D}isambiguation (\textbf{BDTD}). Our method's main idea is to obtain the hidden senses of source words via back deduction from the target language, i.e., employ translation words in the target language to deduce senses of original words identified in the translation procedure. To evaluate BDTD, we conduct an extensive empirical study with millions of sentences under three popular translators, including Google Translate and Bing Microsoft Translator. The experimental results indicate that BDTD can identify a considerable number of WSD bugs with high accuracy, more than 80%, under all three translators.
Wed 19 JulDisplayed time zone: Pacific Time (US & Canada) change
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16:00 10mTalk | ACETest: Automated Constraint Extraction for Testing Deep Learning Operators Technical Papers Jingyi Shi Institute of Information Engineering at Chinese Academy of Sciences; University of Chinese Academy of Sciences, Yang Xiao Institute of Information Engineering at Chinese Academy of Sciences; University of Chinese Academy of Sciences, Yuekang Li University of New South Wales, Yeting Li Institute of Information Engineering at Chinese Academy of Sciences; University of Chinese Academy of Sciences, DongSong Yu Zhongguancun Laboratory, Chendong Yu Institute of Information Engineering at Chinese Academy of Sciences; University of Chinese Academy of Sciences, Hui Su Institute of Information Engineering at Chinese Academy of Sciences; University of Chinese Academy of Sciences, Yufeng Chen Institute of Information Engineering at Chinese Academy of Sciences; University of Chinese Academy of Sciences, Wei Huo Institute of Information Engineering at Chinese Academy of Sciences DOI | ||
16:10 10mTalk | Latent Imitator: Generating Natural Individual Discriminatory Instances for Black-Box Fairness Testing Technical Papers Yisong Xiao Beihang University, Aishan Liu Beihang University; Institute of Dataspace, Li Tianlin Nanyang Technological University, Xianglong Liu Beihang University; Institute of Dataspace; Zhongguancun Laboratory DOI | ||
16:20 10mTalk | CoopHance: Cooperative Enhancement for Robustness of Deep Learning Systems Technical Papers Quan Zhang Tsinghua University, Yongqiang Tian University of Waterloo, Yifeng Ding University of Illinois at Urbana-Champaign, Shanshan Li National University of Defense Technology, Chengnian Sun University of Waterloo, Yu Jiang Tsinghua University, Jiaguang Sun Tsinghua University DOI | ||
16:30 10mTalk | Back Deduction Based Testing for Word Sense Disambiguation Ability of Machine Translation Systems Technical Papers Jun Wang Nanjing University, Yanhui Li Nanjing University, Xiang Huang Nanjing University, Lin Chen Nanjing University, Xiaofang Zhang Soochow University, Yuming Zhou Nanjing University DOI | ||
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