# Incremental LSTM-based Dialog State Tracker

arXiv: https://arxiv.org/abs/1507.03471 · HTML: https://mlsanity.com/paper/1507.03471/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jul 2015.

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

- [Slot Self-Attentive Dialogue State Tracking](https://mlsanity.com/paper/2101.09374/) (2021)
- [Multimodal Incremental Transformer with Visual Grounding for Visual Dialogue Generation](https://mlsanity.com/paper/2109.08478/) (2021)
- [Dialogue State Tracking with Multi-Level Fusion of Predicted Dialogue States and Conversations](https://mlsanity.com/paper/2107.05168/) (2021)
- [Dual Slot Selector via Local Reliability Verification for Dialogue State Tracking](https://mlsanity.com/paper/2107.12578/) (2021)
- [Oh My Mistake!: Toward Realistic Dialogue State Tracking including Turnback Utterances](https://mlsanity.com/paper/2108.12637/) (2021)

## Abstract (arXiv)

A dialog state tracker is an important component in modern spoken dialog systems. We present an incremental dialog state tracker, based on LSTM networks. It directly uses automatic speech recognition hypotheses to track the state. We also present the key non-standard aspects of the model that bring its performance close to the state-of-the-art and experimentally analyze their contribution: including the ASR confidence scores, abstracting scarcely represented values, including transcriptions in the training data, and model averaging.