An empirical assessment of deep learning approaches to task-oriented dialog management

TL;DR

This paper empirically evaluates various deep learning configurations for task-oriented dialog management across multiple domains, highlighting factors influencing accuracy and system performance.

Contribution

It provides a comprehensive assessment of deep learning approaches in dialog management, exploring how different configurations affect effectiveness.

Findings

Feature extraction methods impact accuracy

Input representation and context consideration are crucial

Hyper-parameters significantly influence performance

Abstract

Deep learning is providing very positive results in areas related to conversational interfaces, such as speech recognition, but its potential benefit for dialog management has still not been fully studied. In this paper, we perform an assessment of different configurations for deep-learned dialog management with three dialog corpora from different application domains and varying in size, dimensionality and possible system responses. Our results have allowed us to identify several aspects that can have an impact on accuracy, including the approaches used for feature extraction, input representation, context consideration and the hyper-parameters of the deep neural networks employed.