Abstract
A major challenge in analysing social me-dia data belonging to languages that use non-English script is its code-mixed nature. Recentresearch has presented state-of-the-art contex-tual embedding models (both monolingual s.a.BERT and multilingual s.a.XLM-R) as apromising approach. In this paper, we showthat the performance of such embedding mod-els depends on multiple factors, such as thelevel of code-mixing in the dataset, and thesize of the training dataset. We empiricallyshow that a newly introduced Capsule+biGRUclassifier could outperform a classifier built onthe English-BERT as well as XLM-R just witha training dataset of about 6500 samples forthe Sinhala-English code-mixed data.- Anthology ID:
- 2021.ranlp-1.30
- Volume:
- Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
- Month:
- September
- Year:
- 2021
- Address:
- Held Online
- Venue:
- RANLP
- SIG:
- Publisher:
- INCOMA Ltd.
- Note:
- Pages:
- 256–263
- Language:
- URL:
- https://aclanthology.org/2021.ranlp-1.30
- DOI:
- Cite (ACL):
- Shanaka Chathuranga and Surangika Ranathunga. 2021. Classification of Code-Mixed Text Using Capsule Networks. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 256–263, Held Online. INCOMA Ltd..
- Cite (Informal):
- Classification of Code-Mixed Text Using Capsule Networks (Chathuranga & Ranathunga, RANLP 2021)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2021.ranlp-1.30.pdf