Multi-task learning using a combination of contextualised and static word embeddings for Arabic sarcasm detection and sentiment analysis

Abdullah I. Alharbi, Mark Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)
30 Downloads (Pure)

Abstract

Sarcasm detection and sentiment analysis are important tasks in Natural Language Understanding. Sarcasm is a type of expression where the sentiment polarity is flipped by an interfering factor. In this study, we exploited this relationship to enhance both tasks by proposing a multi-task learning approach using a combination of static and contextualised embeddings. Our proposed system achieved the best result in the sarcasm detection subtask (Abu Farha et al., 2021).

Original languageEnglish
Title of host publicationProceedings of the Sixth Arabic Natural Language Processing Workshop
EditorsNizar Habash, Houda Bouamor, Hazem Hajj, Walid Magdy, Wajdi Zaghouani, Fethi Bougares, Nadi Tomeh, Ibrahim Abu Farha, Samia Touileb
PublisherAssociation for Computational Linguistics, ACL
Pages318-322
Number of pages5
ISBN (Electronic)9781954085091
Publication statusPublished - 30 Apr 2021
Event6th Arabic Natural Language Processing Workshop, WANLP 2021 - Virtual, Kyiv, Ukraine
Duration: 19 Apr 202119 Apr 2021

Publication series

NameWorkshop on Arabic Natural Language Processing (WANLP)

Conference

Conference6th Arabic Natural Language Processing Workshop, WANLP 2021
Country/TerritoryUkraine
CityKyiv
Period19/04/2119/04/21

Bibliographical note

Publisher Copyright:
© WANLP 2021 - 6th Arabic Natural Language Processing Workshop

ASJC Scopus subject areas

  • Language and Linguistics
  • Computational Theory and Mathematics
  • Software
  • Linguistics and Language

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