@inproceedings{sakib-etal-2023-intent,
title = "Intent Detection and Slot Filling for Home Assistants: Dataset and Analysis for {B}angla and {S}ylheti",
author = "Sakib, Fardin Ahsan and
Karim, A H M Rezaul and
Khan, Saadat Hasan and
Rahman, Md Mushfiqur",
editor = "Alam, Firoj and
Kar, Sudipta and
Chowdhury, Shammur Absar and
Sadeque, Farig and
Amin, Ruhul",
booktitle = "Proceedings of the First Workshop on Bangla Language Processing (BLP-2023)",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.banglalp-1.6",
doi = "10.18653/v1/2023.banglalp-1.6",
pages = "48--55",
abstract = "As voice assistants cement their place in our technologically advanced society, there remains a need to cater to the diverse linguistic landscape, including colloquial forms of low-resource languages. Our study introduces the first-ever comprehensive dataset for intent detection and slot filling in formal Bangla, colloquial Bangla, and Sylheti languages, totaling 984 samples across 10 unique intents. Our analysis reveals the robustness of large language models for tackling downstream tasks with inadequate data. The GPT-3.5 model achieves an impressive F1 score of 0.94 in intent detection and 0.51 in slot filling for colloquial Bangla.",
}
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<abstract>As voice assistants cement their place in our technologically advanced society, there remains a need to cater to the diverse linguistic landscape, including colloquial forms of low-resource languages. Our study introduces the first-ever comprehensive dataset for intent detection and slot filling in formal Bangla, colloquial Bangla, and Sylheti languages, totaling 984 samples across 10 unique intents. Our analysis reveals the robustness of large language models for tackling downstream tasks with inadequate data. The GPT-3.5 model achieves an impressive F1 score of 0.94 in intent detection and 0.51 in slot filling for colloquial Bangla.</abstract>
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%0 Conference Proceedings
%T Intent Detection and Slot Filling for Home Assistants: Dataset and Analysis for Bangla and Sylheti
%A Sakib, Fardin Ahsan
%A Karim, A. H. M. Rezaul
%A Khan, Saadat Hasan
%A Rahman, Md Mushfiqur
%Y Alam, Firoj
%Y Kar, Sudipta
%Y Chowdhury, Shammur Absar
%Y Sadeque, Farig
%Y Amin, Ruhul
%S Proceedings of the First Workshop on Bangla Language Processing (BLP-2023)
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F sakib-etal-2023-intent
%X As voice assistants cement their place in our technologically advanced society, there remains a need to cater to the diverse linguistic landscape, including colloquial forms of low-resource languages. Our study introduces the first-ever comprehensive dataset for intent detection and slot filling in formal Bangla, colloquial Bangla, and Sylheti languages, totaling 984 samples across 10 unique intents. Our analysis reveals the robustness of large language models for tackling downstream tasks with inadequate data. The GPT-3.5 model achieves an impressive F1 score of 0.94 in intent detection and 0.51 in slot filling for colloquial Bangla.
%R 10.18653/v1/2023.banglalp-1.6
%U https://aclanthology.org/2023.banglalp-1.6
%U https://doi.org/10.18653/v1/2023.banglalp-1.6
%P 48-55
Markdown (Informal)
[Intent Detection and Slot Filling for Home Assistants: Dataset and Analysis for Bangla and Sylheti](https://aclanthology.org/2023.banglalp-1.6) (Sakib et al., BanglaLP 2023)
ACL