Abstract
This paper presents the different models submitted by the LT@Helsinki team for the SemEval2020 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 dataset. The results show that offensive tweet classification is one of several language-based tasks where BERT can achieve state-of-the-art results.
Original language | English |
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Title of host publication | Proceedings of the Fourteenth Workshop on Semantic Evaluation |
Editors | Aurelie Herbelot, Xiaodan Zhu, Alexis Palmer, Nathan Schneider, Jonathan May, Ekaterina Shutova |
Number of pages | 7 |
Place of Publication | Barcelona |
Publisher | International Committee for Computational Linguistics |
Publication date | 2020 |
Pages | 1569-1575 |
ISBN (Electronic) | 978-1-952148-31-6 |
Publication status | Published - 2020 |
MoE publication type | A4 Article in conference proceedings |
Event | International Workshop on Semantic Evaluation - [Online event], Barcelona, Spain Duration: 12 Dec 2020 → 13 Dec 2020 Conference number: 14 http://alt.qcri.org/semeval2020/ |
Fields of Science
- 113 Computer and information sciences
- 6121 Languages