Abstract
This paper describes approaches to identify Hope Speech in short, informal texts in English, Malayalam and Tamil using different machine learning techniques. We demonstrate that even very simple baseline algorithms perform reasonably well on this task if provided with enough training data. However, our best performing algorithm is a cross-lingual transfer learning approach in which we fine-tune XLM-RoBERTa.- Anthology ID:
- 2021.ltedi-1.19
- Volume:
- Proceedings of the First Workshop on Language Technology for Equality, Diversity and Inclusion
- Month:
- April
- Year:
- 2021
- Address:
- Kyiv
- Editors:
- Bharathi Raja Chakravarthi, John P. McCrae, Manel Zarrouk, Kalika Bali, Paul Buitelaar
- Venue:
- LTEDI
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 132–135
- Language:
- URL:
- https://aclanthology.org/2021.ltedi-1.19
- DOI:
- Cite (ACL):
- Stefan Ziehe, Franziska Pannach, and Aravind Krishnan. 2021. GCDH@LT-EDI-EACL2021: XLM-RoBERTa for Hope Speech Detection in English, Malayalam, and Tamil. In Proceedings of the First Workshop on Language Technology for Equality, Diversity and Inclusion, pages 132–135, Kyiv. Association for Computational Linguistics.
- Cite (Informal):
- GCDH@LT-EDI-EACL2021: XLM-RoBERTa for Hope Speech Detection in English, Malayalam, and Tamil (Ziehe et al., LTEDI 2021)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2021.ltedi-1.19.pdf