Action State Update Approach to Dialogue Management

• URL: http://arxiv.org/abs/2011.04637v2
• Date: Tue, 10 Nov 2020 17:03:22 GMT
• Title: Action State Update Approach to Dialogue Management
• Authors: Svetlana Stoyanchev, Simon Keizer and Rama Doddipatla
• Abstract summary: We propose the action state update approach (ASU) for utterance interpretation. Our goal is to interpret referring expressions in user input without a domain-specific natural language understanding component. With both user-simulated and interactive human evaluations, we show that the ASU approach successfully interprets user utterances in a dialogue system.
• Score: 16.602804535683553
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Utterance interpretation is one of the main functions of a dialogue manager, which is the key component of a dialogue system. We propose the action state update approach (ASU) for utterance interpretation, featuring a statistically trained binary classifier used to detect dialogue state update actions in the text of a user utterance. Our goal is to interpret referring expressions in user input without a domain-specific natural language understanding component. For training the model, we use active learning to automatically select simulated training examples. With both user-simulated and interactive human evaluations, we show that the ASU approach successfully interprets user utterances in a dialogue system, including those with referring expressions.

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