Corpus ID: 221135886

Language Models as Few-Shot Learner for Task-Oriented Dialogue Systems

@article{Madotto2020LanguageMA,
  title={Language Models as Few-Shot Learner for Task-Oriented Dialogue Systems},
  author={Andrea Madotto and Zihan Liu},
  journal={ArXiv},
  year={2020},
  volume={abs/2008.06239}
}

Andrea Madotto, Zihan Liu

Published 2020

Computer Science

ArXiv

Task-oriented dialogue systems use four connected modules, namely, Natural Language Understanding (NLU), a Dialogue State Tracking (DST), Dialogue Policy (DP) and Natural Language Generation (NLG). A research challenge is to learn each module with the least amount of samples (i.e., few-shots) given the high cost related to the data collection. The most common and effective technique to solve this problem is transfer learning, where large language models, either pre-trained on text or task… CONTINUE READING

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