Abstract
This paper presents six document classification models using the latest transformer encoders and a high-performing ensemble model for a task of offensive language identification in social media. For the individual models, deep transformer layers are applied to perform multi-head attentions. For the ensemble model, the utterance representations taken from those individual models are concatenated and fed into a linear decoder to make the final decisions. Our ensemble model outperforms the individual models and shows up to 8.6% improvement over the individual models on the development set. On the test set, it achieves macro-F1 of 90.9% and becomes one of the high performing systems among 85 participants in the sub-task A of this shared task. Our analysis shows that although the ensemble model significantly improves the accuracy on the development set, the improvement is not as evident on the test set.- Anthology ID:
- 2020.semeval-1.299
- Volume:
- Proceedings of the Fourteenth Workshop on Semantic Evaluation
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona (online)
- Editors:
- Aurelie Herbelot, Xiaodan Zhu, Alexis Palmer, Nathan Schneider, Jonathan May, Ekaterina Shutova
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- International Committee for Computational Linguistics
- Note:
- Pages:
- 2244–2250
- Language:
- URL:
- https://preview.aclanthology.org/remove-affiliations/2020.semeval-1.299/
- DOI:
- 10.18653/v1/2020.semeval-1.299
- Cite (ACL):
- Xiangjue Dong and Jinho D. Choi. 2020. XD at SemEval-2020 Task 12: Ensemble Approach to Offensive Language Identification in Social Media Using Transformer Encoders. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 2244–2250, Barcelona (online). International Committee for Computational Linguistics.
- Cite (Informal):
- XD at SemEval-2020 Task 12: Ensemble Approach to Offensive Language Identification in Social Media Using Transformer Encoders (Dong & Choi, SemEval 2020)
- PDF:
- https://preview.aclanthology.org/remove-affiliations/2020.semeval-1.299.pdf
- Data
- OLID