Tracking of enriched dialog states for flexible conversational information access

TL;DR

This paper introduces an enriched dialog state tracking (EDST) method that enhances dialog state representation to support more flexible and realistic conversational tasks, including searching and enquiring about movies.

Contribution

The paper proposes a novel EDST approach that overcomes limitations of traditional slot-value dialog states and introduces a new movie dialog dataset for more versatile information access.

Findings

EDST outperforms state-of-the-art DST methods on multiple datasets

Supports multiple task types including searching and enquiring

Achieves high accuracy on Iqiyi, WOZ2.0, and DSTC2 datasets

Abstract

Dialog state tracking (DST) is a crucial component in a task-oriented dialog system for conversational information access. A common practice in current dialog systems is to define the dialog state by a set of slot-value pairs. Such representation of dialog states and the slot-filling based DST have been widely employed, but suffer from three drawbacks. (1) The dialog state can contain only a single value for a slot, and (2) can contain only users' affirmative preference over the values for a slot. (3) Current task-based dialog systems mainly focus on the searching task, while the enquiring task is also very common in practice. The above observations motivate us to enrich current representation of dialog states and collect a brand new dialog dataset about movies, based upon which we build a new DST, called enriched DST (EDST), for flexible accessing movie information. The EDST supports the searching task, the enquiring task and their mixed task. We show that the new EDST method not only achieves good results on Iqiyi dataset, but also outperforms other state-of-the-art DST methods on the traditional dialog datasets, WOZ2.0 and DSTC2.