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        <title>Incremental processing of noisy user utterances in the spoken language understanding task</title>
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    <name type="personal">
        <namePart type="given">Stefan</namePart>
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        <namePart type="given">Jan</namePart>
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    <name type="personal">
        <namePart type="given">Alex</namePart>
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            <title>Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)</title>
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        <name type="personal">
            <namePart type="given">Wei</namePart>
            <namePart type="family">Xu</namePart>
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            <namePart type="given">Alan</namePart>
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            <namePart type="given">Tim</namePart>
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    <abstract>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.</abstract>
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    <identifier type="doi">10.18653/v1/D19-5535</identifier>
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        <date>2019-11</date>
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%0 Conference Proceedings
%T Incremental processing of noisy user utterances in the spoken language understanding task
%A Constantin, Stefan
%A Niehues, Jan
%A Waibel, Alex
%Y Xu, Wei
%Y Ritter, Alan
%Y Baldwin, Tim
%Y Rahimi, Afshin
%S Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F constantin-etal-2019-incremental
%X 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.
%R 10.18653/v1/D19-5535
%U https://aclanthology.org/D19-5535
%U https://doi.org/10.18653/v1/D19-5535
%P 265-274

Markdown (Informal)

[Incremental processing of noisy user utterances in the spoken language understanding task](https://aclanthology.org/D19-5535) (Constantin et al., WNUT 2019)

• Incremental processing of noisy user utterances in the spoken language understanding task (Constantin et al., WNUT 2019)

ACL

• Stefan Constantin, Jan Niehues, and Alex Waibel. 2019. Incremental processing of noisy user utterances in the spoken language understanding task. In Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019), pages 265–274, Hong Kong, China. Association for Computational Linguistics.