LT@Helsinki at SemEval-2020 Task 12: Multilingual or language-specific BERT?

Marc Pàmies, Emily Öhman, Kaisla Kajava, Jörg Tiedemann

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

1 Citation (Scopus)

Abstract

This paper presents the different models submitted by the LT@Helsinki team for the SemEval 2020 Shared Task 12. Our team participated in sub-tasks A and C; titled offensive language identification and offense target identification, respectively. In both cases we used the so-called Bidirectional Encoder Representation from Transformer (BERT), a model pre-trained by Google and fine-tuned by us on the OLID and SOLID datasets. The results show that offensive tweet classification is one of several language-based tasks where BERT can achieve state-of-the-art results.

Original languageEnglish
Title of host publication14th International Workshops on Semantic Evaluation, SemEval 2020 - co-located 28th International Conference on Computational Linguistics, COLING 2020, Proceedings
EditorsAurelie Herbelot, Xiaodan Zhu, Alexis Palmer, Nathan Schneider, Jonathan May, Ekaterina Shutova
PublisherInternational Committee for Computational Linguistics
Pages1569-1575
Number of pages7
ISBN (Electronic)9781952148316
Publication statusPublished - 2020
Externally publishedYes
Event14th International Workshops on Semantic Evaluation, SemEval 2020 - Barcelona, Spain
Duration: 2020 Dec 122020 Dec 13

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
CityBarcelona
Period20/12/1220/12/13

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computational Theory and Mathematics
  • Computer Science Applications

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