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)
- Venue:
- SemEval
- SIG:
- SIGLEX
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/starsem-semeval-split/2020.semeval-1.279.pdf
- Data
- OLID