Natural Language Understanding (NLU) is crucial for conversational AI, yet low-resource languages lag behind in essential tasks like intent detection and slot-filling. To address this gap, we converted the widely-used English SNIPS dataset to Bangla using LLaMA 3, creating a dataset that captures the linguistic complexities of the language. With this translated dataset for model training, our experimental evaluation compares both independent and joint modeling approaches using transformer architecture. Results demonstrate that a joint approach based on multilingual BERT (mBERT) achieves superior performance, with 97.83% intent accuracy and 91.03% F1 score for slot filling. This work advances NLU capabilities for Bangla and provides insights for developing robust models in other low-resource languages.

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.