Abstract
In recent years, several systems have been developed to regulate the spread of negativity and eliminate aggressive, offensive or abusive contents from the online platforms. Nevertheless, a limited number of researches carried out to identify positive, encouraging and supportive contents. In this work, our goal is to identify whether a social media post/comment contains hope speech or not. We propose three distinct models to identify hope speech in English, Tamil and Malayalam language to serve this purpose. To attain this goal, we employed various machine learning (SVM, LR, ensemble), deep learning (CNN+BiLSTM) and transformer (m-BERT, Indic-BERT, XLNet, XLM-R) based methods. Results indicate that XLM-R outdoes all other techniques by gaining a weighted f_1-score of 0.93, 0.60 and 0.85 respectively for English, Tamil and Malayalam language. Our team has achieved 1st, 2nd and 1st rank in these three tasks respectively.- Anthology ID:
- 2021.ltedi-1.25
- 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:
- 168–174
- Language:
- URL:
- https://aclanthology.org/2021.ltedi-1.25
- DOI:
- Cite (ACL):
- Eftekhar Hossain, Omar Sharif, and Mohammed Moshiul Hoque. 2021. NLP-CUET@LT-EDI-EACL2021: Multilingual Code-Mixed Hope Speech Detection using Cross-lingual Representation Learner. In Proceedings of the First Workshop on Language Technology for Equality, Diversity and Inclusion, pages 168–174, Kyiv. Association for Computational Linguistics.
- Cite (Informal):
- NLP-CUET@LT-EDI-EACL2021: Multilingual Code-Mixed Hope Speech Detection using Cross-lingual Representation Learner (Hossain et al., LTEDI 2021)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2021.ltedi-1.25.pdf
- Code
- eftekhar-hossain/CUET_NLP-EACL_2021