Abstract
Hope is an essential aspect of mental health stability and recovery in every individual in this fast-changing world. Any tools and methods developed for detection, analysis, and generation of hope speech will be beneficial. In this paper, we propose a model on hope-speech detection to automatically detect web content that may play a positive role in diffusing hostility on social media. We perform the experiments by taking advantage of pre-processing and transfer-learning models. We observed that the pre-trained multilingual-BERT model with convolution neural networks gave the best results. Our model ranked first, third, and fourth ranks on English, Malayalam-English, and Tamil-English code-mixed datasets.- Anthology ID:
- 2021.ltedi-1.11
- 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:
- 86–91
- Language:
- URL:
- https://aclanthology.org/2021.ltedi-1.11
- DOI:
- Cite (ACL):
- Suman Dowlagar and Radhika Mamidi. 2021. EDIOne@LT-EDI-EACL2021: Pre-trained Transformers with Convolutional Neural Networks for Hope Speech Detection.. In Proceedings of the First Workshop on Language Technology for Equality, Diversity and Inclusion, pages 86–91, Kyiv. Association for Computational Linguistics.
- Cite (Informal):
- EDIOne@LT-EDI-EACL2021: Pre-trained Transformers with Convolutional Neural Networks for Hope Speech Detection. (Dowlagar & Mamidi, LTEDI 2021)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/2021.ltedi-1.11.pdf