On the "Easy" Task of Evaluating Chinese Irony Detection

Anran Li, Emmanuele Chersoni, Rong Xiang, Chu-ren Huang, Qin Lu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review


In this paper, we present a discussion on the problem in the evaluation of irony detection in Mandarin Chinese, especially due to the difficulties of finding an exhaustive definition and to the current lack of a gold standard for computational models. We describe some preliminary results of our experiments on an irony detection system for Chinese, and analyze examples of irony or other related phenomena that turned out to be challenging for NLP classifiers.
Original languageEnglish
Title of host publicationThe 33rd Pacific Asia Conference on Language, Information and Computation (PACLIC 2019)
Pages452 – 460
Publication statusPublished - Sept 2019


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