Abstract
This paper introduces an approach to stress identification in Tamil and Telugu, leveraging traditional machine learning models—Fasttext for Tamil and Naive Bayes for Telugu—yielding commendable results. The study highlights the scarcity of annotated data and recognizes limitations in phonetic features relevant to these languages, impacting precise information extraction. Our models achieved a macro F1 score of 0.77 for Tamil and 0.72 for Telugu with Fasttext and Naive Bayes, respectively. While the Telugu model secured the second rank in shared tasks, ongoing research is crucial to unlocking the full potential of stress identification in these languages, necessitating the exploration of additional features and advanced techniques specified in the discussions and limitations section.- Anthology ID:
- 2024.ltedi-1.15
- Volume:
- Proceedings of the Fourth Workshop on Language Technology for Equality, Diversity, Inclusion
- Month:
- March
- Year:
- 2024
- Address:
- St. Julian's, Malta
- Editors:
- Bharathi Raja Chakravarthi, Bharathi B, Paul Buitelaar, Thenmozhi Durairaj, György Kovács, Miguel Ángel García Cumbreras
- Venues:
- LTEDI | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 152–156
- Language:
- URL:
- https://aclanthology.org/2024.ltedi-1.15
- DOI:
- Cite (ACL):
- Anvi Alex Eponon, Ildar Batyrshin, and Grigori Sidorov. 2024. Pinealai_StressIdent_LT-EDI@EACL2024: Minimal configurations for Stress Identification in Tamil and Telugu. In Proceedings of the Fourth Workshop on Language Technology for Equality, Diversity, Inclusion, pages 152–156, St. Julian's, Malta. Association for Computational Linguistics.
- Cite (Informal):
- Pinealai_StressIdent_LT-EDI@EACL2024: Minimal configurations for Stress Identification in Tamil and Telugu (Alex Eponon et al., LTEDI-WS 2024)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2024.ltedi-1.15.pdf