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:
Bibkey:
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)
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PDF:
https://preview.aclanthology.org/nschneid-patch-1/2022.lrec-1.623.pdf