# Tracking of enriched dialog states for flexible conversational information access

arXiv: https://arxiv.org/abs/1711.03381 · HTML: https://mlsanity.com/paper/1711.03381/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2017.

## Built on by (most cited first, 5 of 5)

- [Preview, Attend and Review: Schema-Aware Curriculum Learning for Multi-Domain Dialog State Tracking](https://mlsanity.com/paper/2106.00291/) (2021)
- [CGoDial: A Large-Scale Benchmark for Chinese Goal-oriented Dialog Evaluation](https://mlsanity.com/paper/2211.11617/) (2022)
- [Building Markovian Generative Architectures over Pretrained LM Backbones for Efficient Task-Oriented Dialog Systems](https://mlsanity.com/paper/2204.06452/) (2022)
- [Information Extraction and Human-Robot Dialogue towards Real-life Tasks: A Baseline Study with the MobileCS Dataset](https://mlsanity.com/paper/2209.13464/) (2022)
- [Prompt Pool based Class-Incremental Continual Learning for Dialog State Tracking](https://mlsanity.com/paper/2311.10271/) (2023)

## Abstract (arXiv)

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.