# Modeling ASR Ambiguity for Dialogue State Tracking Using Word Confusion Networks

arXiv: https://arxiv.org/abs/2002.00768 · HTML: https://mlsanity.com/paper/2002.00768/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Feb 2020.

## Built on by (most cited first, 2 of 2)

- [OLISIA: a Cascade System for Spoken Dialogue State Tracking](https://mlsanity.com/paper/2304.11073/) (2023)
- [Learning Domain Specific Language Models for Automatic Speech Recognition through Machine Translation](https://mlsanity.com/paper/2110.10261/) (2021)

## Abstract (arXiv)

Spoken dialogue systems typically use a list of top-N ASR hypotheses for inferring the semantic meaning and tracking the state of the dialogue. However ASR graphs, such as confusion networks (confnets), provide a compact representation of a richer hypothesis space than a top-N ASR list. In this paper, we study the benefits of using confusion networks with a state-of-the-art neural dialogue state tracker (DST). We encode the 2-dimensional confnet into a 1-dimensional sequence of embeddings using an attentional confusion network encoder which can be used with any DST system. Our confnet encoder is plugged into the state-of-the-art 'Global-locally Self-Attentive Dialogue State Tacker' (GLAD) model for DST and obtains significant improvements in both accuracy and inference time compared to using top-N ASR hypotheses.