Intent Detection and Slot Filling for Home Assistants: Dataset and Analysis for Bangla and Sylheti
This addresses the problem of limited linguistic diversity in voice assistants for speakers of Bangla and Sylheti, but it is incremental as it applies existing methods to new data.
The study tackled the lack of datasets for intent detection and slot filling in low-resource languages by introducing the first comprehensive dataset for formal Bangla, colloquial Bangla, and Sylheti, with 984 samples across 10 intents, and found that GPT-3.5 achieved an F1 score of 0.94 in intent detection and 0.51 in slot filling for colloquial Bangla.
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.