Intent Detection and Slot Filling for Home Assistants

This is the official repository for the paper "Intent Detection and Slot Filling for Home Assistants: Dataset and Analysis for Bangla and Sylheti"

Link to original paper.

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.

Please cite the following:

@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",
    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"
}