Intent Detection and Slot Filling for Vietnamese
This work addresses a gap for Vietnamese language processing, but it is incremental as it builds on existing methods for a low-resource domain.
The authors tackled the lack of resources for Vietnamese in intent detection and slot filling by creating the first public dataset and proposing a joint model that extends JointBERT+CRF with an intent-slot attention layer, resulting in significant performance improvements over the baseline.
Intent detection and slot filling are important tasks in spoken and natural language understanding. However, Vietnamese is a low-resource language in these research topics. In this paper, we present the first public intent detection and slot filling dataset for Vietnamese. In addition, we also propose a joint model for intent detection and slot filling, that extends the recent state-of-the-art JointBERT+CRF model with an intent-slot attention layer to explicitly incorporate intent context information into slot filling via "soft" intent label embedding. Experimental results on our Vietnamese dataset show that our proposed model significantly outperforms JointBERT+CRF. We publicly release our dataset and the implementation of our model at: https://github.com/VinAIResearch/JointIDSF