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.
@article{arxiv.2011.04637,
title = {Action State Update Approach to Dialogue Management},
author = {Svetlana Stoyanchev and Simon Keizer and Rama Doddipatla},
journal= {arXiv preprint arXiv:2011.04637},
year = {2020}
}