INTENT RECOGNITION AND UNSUPERVISED SLOT IDENTIFICATION FOR LOW-RESOURCED SPOKEN DIALOG SYSTEMS

Intent Recognition and Slot Identification are crucial components in spoken language understanding (SLU) systems. In this paper, we present a novel approach towards both these tasks in the context of low-resourced and unwritten languages. We use an acoustic based SLU system that converts speech to its phonetic transcription using a universal phone recognition system. We build a word-free natural language understanding module that does intent recognition and slot identification from these phonetic transcription. Our proposed SLU system performs competitively for resource rich scenarios and significantly outperforms existing approaches as the amount of available data reduces. We train both recurrent and transformer based neural networks and test our system on five natural speech datasets in five different languages. We observe more than 10% improvement for intent classification in Tamil and more than 5% improvement for intent classification in Sinhala. Additionally, we present a novel approach towards unsupervised slot identification using normalized attention scores. This approach can be used for unsupervised slot labelling, data augmentation and to generate data for a new slot in a one-shot way with only one speech recording.