Incremental processing of noisy user utterances in the spoken language understanding task
This addresses latency issues for real-world spoken language applications, though it is incremental as it builds on existing neural network architectures.
The paper tackles the high latency in spoken language understanding systems by proposing a model-agnostic method for incremental processing of partial utterances, resulting in improvements of up to 47.91 absolute percentage points in F1-score.
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.