# Incremental processing of noisy user utterances in the spoken language understanding task

arXiv: https://arxiv.org/abs/1909.13790 · HTML: https://mlsanity.com/paper/1909.13790/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2019.

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

- [Real-time Caller Intent Detection In Human-Human Customer Support Spoken Conversations](https://mlsanity.com/paper/2208.06802/) (2022)

## Abstract (arXiv)

The state-of-the-art neural network architectures make it possible to create spoken language understanding systems with high quality and fast processing time. One major challenge for real-world applications is the high latency of these systems caused by triggered actions with high executions times. If an action can be separated into subactions, the reaction time of the systems can be improved through incremental processing of the user utterance and starting subactions while the utterance is still being uttered. In this work, we present a model-agnostic method to achieve high quality in processing incrementally produced partial utterances. Based on clean and noisy versions of the ATIS dataset, we show how to create datasets with our method to create low-latency natural language understanding components. We get improvements of up to 47.91 absolute percentage points in the metric F1-score.