Abstract
This paper describes the result of team-Maoqin at DravidianLangTech-EACL2021. The provided task consists of three languages(Tamil, Malayalam, and Kannada), I only participate in one of the language task-Malayalam. The goal of this task is to identify offensive language content of the code-mixed dataset of comments/posts in Dravidian Languages (Tamil-English, Malayalam-English, and Kannada-English) collected from social media. This is a classification task at the comment/post level. Given a Youtube comment, systems have to classify it into Not-offensive, Offensive-untargeted, Offensive-targeted-individual, Offensive-targeted-group, Offensive-targeted-other, or Not-in-indented-language. I use the transformer-based language model with BiGRU-Attention to complete this task. To prove the validity of the model, I also use some other neural network models for comparison. And finally, the team ranks 5th in this task with a weighted average F1 score of 0.93 on the private leader board.- Anthology ID:
- 2021.dravidianlangtech-1.40
- Volume:
- Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages
- Month:
- April
- Year:
- 2021
- Address:
- Kyiv
- Editors:
- Bharathi Raja Chakravarthi, Ruba Priyadharshini, Anand Kumar M, Parameswari Krishnamurthy, Elizabeth Sherly
- Venue:
- DravidianLangTech
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 281–286
- Language:
- URL:
- https://aclanthology.org/2021.dravidianlangtech-1.40
- DOI:
- Cite (ACL):
- Maoqin Yang. 2021. Maoqin @ DravidianLangTech-EACL2021: The Application of Transformer-Based Model. In Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages, pages 281–286, Kyiv. Association for Computational Linguistics.
- Cite (Informal):
- Maoqin @ DravidianLangTech-EACL2021: The Application of Transformer-Based Model (Yang, DravidianLangTech 2021)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2021.dravidianlangtech-1.40.pdf