Abstract
Analysis and deciphering code-mixed data is imperative in academia and industry, in a multilingual country like India, in order to solve problems apropos Natural Language Processing. This paper proposes a bidirectional long short-term memory (BiLSTM) with the attention-based approach, in solving the hope speech detection problem. Using this approach an F1-score of 0.73 (9thrank) in the Malayalam-English data set was achieved from a total of 31 teams who participated in the competition.- Anthology ID:
- 2021.ltedi-1.22
- 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:
- 149–156
- Language:
- URL:
- https://aclanthology.org/2021.ltedi-1.22
- DOI:
- Cite (ACL):
- Thara S, Ravi teja Tasubilli, and Kothamasu Sai rahul. 2021. Amrita@LT-EDI-EACL2021: Hope Speech Detection on Multilingual Text. In Proceedings of the First Workshop on Language Technology for Equality, Diversity and Inclusion, pages 149–156, Kyiv. Association for Computational Linguistics.
- Cite (Informal):
- Amrita@LT-EDI-EACL2021: Hope Speech Detection on Multilingual Text (S et al., LTEDI 2021)
- PDF:
- https://preview.aclanthology.org/corrections-2024-07/2021.ltedi-1.22.pdf