Intent Recognition and Unsupervised Slot Identification for Low Resourced Spoken Dialog Systems
This addresses the problem of building spoken language understanding systems for low-resource and unwritten languages, with incremental improvements in performance.
The paper tackles intent recognition and slot identification for low-resource spoken dialog systems by proposing an acoustic-based SLU system that uses phonetic transcriptions, achieving over 10% improvement in intent classification for Tamil and over 5% for Sinhala as data reduces.
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 present 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 observe more than 10% improvement for intent classification in Tamil and more than 5% improvement for intent classification in Sinhala. We also 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