# Action State Update Approach to Dialogue Management

arXiv: https://arxiv.org/abs/2011.04637 · HTML: https://mlsanity.com/paper/2011.04637/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2020.

## Built on by (most cited first, 4 of 4)

- [Resolving Indirect Referring Expressions for Entity Selection](https://mlsanity.com/paper/2212.10933/) (2022)
- [Adversarial learning of neural user simulators for dialogue policy optimisation](https://mlsanity.com/paper/2306.00858/) (2023)
- [Dialogue Strategy Adaptation to New Action Sets Using Multi-dimensional Modelling](https://mlsanity.com/paper/2204.07082/) (2022)
- [Towards Handling Unconstrained User Preferences in Dialogue](https://mlsanity.com/paper/2109.08650/) (2021)

## Abstract (arXiv)

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.