# Neural Dialogue State Tracking with Temporally Expressive Networks

arXiv: https://arxiv.org/abs/2009.07615 · HTML: https://mlsanity.com/paper/2009.07615/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2020.

## Built on by (most cited first, 1 of 1)

- [Back to the Future: Bidirectional Information Decoupling Network for Multi-turn Dialogue Modeling](https://mlsanity.com/paper/2204.08152/) (2022)

## Abstract (arXiv)

Dialogue state tracking (DST) is an important part of a spoken dialogue system. Existing DST models either ignore temporal feature dependencies across dialogue turns or fail to explicitly model temporal state dependencies in a dialogue. In this work, we propose Temporally Expressive Networks (TEN) to jointly model the two types of temporal dependencies in DST. The TEN model utilizes the power of recurrent networks and probabilistic graphical models. Evaluating on standard datasets, TEN is demonstrated to be effective in improving the accuracy of turn-level-state prediction and the state aggregation.