ECNU at SemEval-2020 Task 7: Assessing Humor in Edited News Headlines Using BiLSTM with Attention

Tiantian Zhang, Zhixuan Chen, Man Lan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

Abstract

In this paper we describe our system submitted to SemEval 2020 Task 7: “Assessing Humor in Edited News Headlines”. We participated in all subtasks, in which the main goal is to predict the mean funniness of the edited headline given the original and the edited headline. Our system involves two similar sub-networks, which generate vector representations for the original and edited headlines respectively. And then we do a subtract operation of the outputs from two sub-networks to predict the funniness of the edited headline.

Original languageEnglish
Title of host publicationCOLING 2020 - The International Workshop on Semantic Evaluation, Proceedings of the 14th Workshop
EditorsAurelie Herbelot, Xiaodan Zhu, Alexis Palmer, Nathan Schneider, Jonathan May, Ekaterina Shutova
PublisherInternational Committee for Computational Linguistics
Pages995-1000
Number of pages6
ISBN (Electronic)9781952148316
DOIs
StatePublished - 2020
Event14th International Workshops on Semantic Evaluation, SemEval 2020 - Virtual, Online, Spain
Duration: 12 Dec 202013 Dec 2020

Publication series

Name14th International Workshops on Semantic Evaluation, SemEval 2020 - co-located 28th International Conference on Computational Linguistics, COLING 2020, Proceedings

Conference

Conference14th International Workshops on Semantic Evaluation, SemEval 2020
Country/TerritorySpain
CityVirtual, Online
Period12/12/2013/12/20

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