Learning to Classify Intents and Slot Labels Given a Handful of Examples

  title={Learning to Classify Intents and Slot Labels Given a Handful of Examples},
  author={Jason Krone and Yi Zhang and Mona T. Diab},
Intent classification (IC) and slot filling (SF) are core components in most goal-oriented dialogue systems. Current IC/SF models perform poorly when the number of training examples per class is small. We propose a new few-shot learning task, few-shot IC/SF, to study and improve the performance of IC and SF models on classes not seen at training time in ultra low resource scenarios. We establish a few-shot IC/SF benchmark by defining few-shot splits for three public IC/SF datasets, ATIS, TOP… 

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