A Study on Prompt-based Few-Shot Learning Methods for Belief State Tracking in Task-oriented Dialog Systems

• URL: http://arxiv.org/abs/2204.08167v1
• Date: Mon, 18 Apr 2022 05:29:54 GMT
• Title: A Study on Prompt-based Few-Shot Learning Methods for Belief State Tracking in Task-oriented Dialog Systems
• Authors: Debjoy Saha, Bishal Santra, Pawan Goyal
• Abstract summary: We tackle the Dialogue Belief State Tracking problem of task-oriented conversational systems. Recent approaches to this problem leveraging Transformer-based models have yielded great results. We explore prompt-based few-shot learning for Dialogue Belief State Tracking.
• Score: 10.024834304960846
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We tackle the Dialogue Belief State Tracking(DST) problem of task-oriented conversational systems. Recent approaches to this problem leveraging Transformer-based models have yielded great results. However, training these models is expensive, both in terms of computational resources and time. Additionally, collecting high quality annotated dialogue datasets remains a challenge for researchers because of the extensive annotation required for training these models. Driven by the recent success of pre-trained language models and prompt-based learning, we explore prompt-based few-shot learning for Dialogue Belief State Tracking. We formulate the DST problem as a 2-stage prompt-based language modelling task and train language models for both tasks and present a comprehensive empirical analysis of their separate and joint performance. We demonstrate the potential of prompt-based methods in few-shot learning for DST and provide directions for future improvement.

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