CG-BERT: Conditional Text Generation With BERT For Generalized Few-shot Intent Detection

Xia Congying, Zhang Chenwei, Nguyen Hoang, Zhang Jiawei, Yu Philip. Arxiv 2020

[Paper]    
Applications BERT Few Shot Language Modeling Model Architecture RAG Reinforcement Learning

In this paper, we formulate a more realistic and difficult problem setup for the intent detection task in natural language understanding, namely Generalized Few-Shot Intent Detection (GFSID). GFSID aims to discriminate a joint label space consisting of both existing intents which have enough labeled data and novel intents which only have a few examples for each class. To approach this problem, we propose a novel model, Conditional Text Generation with BERT (CG-BERT). CG-BERT effectively leverages a large pre-trained language model to generate text conditioned on the intent label. By modeling the utterance distribution with variational inference, CG-BERT can generate diverse utterances for the novel intents even with only a few utterances available. Experimental results show that CG-BERT achieves state-of-the-art performance on the GFSID task with 1-shot and 5-shot settings on two real-world datasets.

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