Abstract
This paper describes our approach to the task of identifying offensive languages in a multilingual setting. We investigate two data augmentation strategies: using additional semi-supervised labels with different thresholds and cross-lingual transfer with data selection. Leveraging the semi-supervised dataset resulted in performance improvements compared to the baseline trained solely with the manually-annotated dataset. We propose a new metric, Translation Embedding Distance, to measure the transferability of instances for cross-lingual data selection. We also introduce various preprocessing steps tailored for social media text along with methods to fine-tune the pre-trained multilingual BERT (mBERT) for offensive language identification. Our multilingual systems achieved competitive results in Greek, Danish, and Turkish at OffensEval 2020.- Anthology ID:
- 2020.semeval-1.206
- 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:
- 1576–1586
- Language:
- URL:
- https://aclanthology.org/2020.semeval-1.206
- DOI:
- 10.18653/v1/2020.semeval-1.206
- Cite (ACL):
- Hwijeen Ahn, Jimin Sun, Chan Young Park, and Jungyun Seo. 2020. NLPDove at SemEval-2020 Task 12: Improving Offensive Language Detection with Cross-lingual Transfer. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 1576–1586, Barcelona (online). International Committee for Computational Linguistics.
- Cite (Informal):
- NLPDove at SemEval-2020 Task 12: Improving Offensive Language Detection with Cross-lingual Transfer (Ahn et al., SemEval 2020)
- PDF:
- https://preview.aclanthology.org/landing_page/2020.semeval-1.206.pdf
- Code
- hwijeen/OffensEval2020
- Data
- OLID