  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.
