NTU_NLP at SemEval-2020 Task 12: Identifying Offensive Tweets Using Hierarchical Multi-Task Learning Approach

Po-Chun Chen, Hen-Hsen Huang, Hsin-Hsi Chen


Abstract
This paper presents our hierarchical multi-task learning (HMTL) and multi-task learning (MTL) approaches for improving the text encoder in Sub-tasks A, B, and C of Multilingual Offensive Language Identification in Social Media (SemEval-2020 Task 12). We show that using the MTL approach can greatly improve the performance of complex problems, i.e. Sub-tasks B and C. Coupled with a hierarchical approach, the performances are further improved. Overall, our best model, HMTL outperforms the baseline model by 3% and 2% of Macro F-score in Sub-tasks B and C of OffensEval 2020, respectively.
Anthology ID:
2020.semeval-1.279
Volume:
Proceedings of the Fourteenth Workshop on Semantic Evaluation
Month:
December
Year:
2020
Address:
Barcelona (online)
Venues:
COLING | SemEval
SIGs:
SIGLEX | SIGSEM
Publisher:
International Committee for Computational Linguistics
Note:
Pages:
2105–2110
Language:
URL:
https://aclanthology.org/2020.semeval-1.279
DOI:
10.18653/v1/2020.semeval-1.279
Bibkey:
Cite (ACL):
Po-Chun Chen, Hen-Hsen Huang, and Hsin-Hsi Chen. 2020. NTU_NLP at SemEval-2020 Task 12: Identifying Offensive Tweets Using Hierarchical Multi-Task Learning Approach. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 2105–2110, Barcelona (online). International Committee for Computational Linguistics.
Cite (Informal):
NTU_NLP at SemEval-2020 Task 12: Identifying Offensive Tweets Using Hierarchical Multi-Task Learning Approach (Chen et al., SemEval 2020)
Copy Citation:
PDF:
https://preview.aclanthology.org/update-css-js/2020.semeval-1.279.pdf
Data
OLID