SHONGLAP: A Large Bengali Open-Domain Dialogue Corpus
Syed Mostofa Monsur, Sakib Chowdhury, Md Shahrar Fatemi, Shafayat Ahmed
Abstract
We introduce SHONGLAP, a large annotated open-domain dialogue corpus in Bengali language. Due to unavailability of high-quality dialogue datasets for low-resource languages like Bengali, existing neural open-domain dialogue systems suffer from data scarcity. We propose a framework to prepare large-scale open-domain dialogue datasets from publicly available multi-party discussion podcasts, talk-shows and label them based on weak-supervision techniques which is particularly suitable for low-resource settings. Using this framework, we prepared our corpus, the first reported Bengali open-domain dialogue corpus (7.7k+ fully annotated dialogues in total) which can serve as a strong baseline for future works. Experimental results show that our corpus improves performance of large language models (BanglaBERT) in case of downstream classification tasks during fine-tuning.- Anthology ID:
- 2022.lrec-1.623
- Volume:
- Proceedings of the Thirteenth Language Resources and Evaluation Conference
- Month:
- June
- Year:
- 2022
- Address:
- Marseille, France
- Editors:
- Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 5797–5804
- Language:
- URL:
- https://aclanthology.org/2022.lrec-1.623
- DOI:
- Cite (ACL):
- Syed Mostofa Monsur, Sakib Chowdhury, Md Shahrar Fatemi, and Shafayat Ahmed. 2022. SHONGLAP: A Large Bengali Open-Domain Dialogue Corpus. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 5797–5804, Marseille, France. European Language Resources Association.
- Cite (Informal):
- SHONGLAP: A Large Bengali Open-Domain Dialogue Corpus (Monsur et al., LREC 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2022.lrec-1.623.pdf