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        <title>Reliable and Interpretable Drift Detection in Streams of Short Texts</title>
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        <namePart type="given">Ella</namePart>
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    <name type="personal">
        <namePart type="given">Matan</namePart>
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    <name type="personal">
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        <namePart type="family">Anaby Tavor</namePart>
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            <title>Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 5: Industry Track)</title>
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            <namePart type="given">Sunayana</namePart>
            <namePart type="family">Sitaram</namePart>
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            <namePart type="given">Beata</namePart>
            <namePart type="family">Beigman Klebanov</namePart>
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    <abstract>Data drift is the change in model input data that is one of the key factors leading to machine learning models performance degradation over time. Monitoring drift helps detecting these issues and preventing their harmful consequences. Meaningful drift interpretation is a fundamental step towards effective re-training of the model. In this study we propose an end-to-end framework for reliable model-agnostic change-point detection and interpretation in large task-oriented dialog systems, proven effective in multiple customer deployments. We evaluate our approach and demonstrate its benefits with a novel variant of intent classification training dataset, simulating customer requests to a dialog system. We make the data publicly available.</abstract>
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    <identifier type="doi">10.18653/v1/2023.acl-industry.42</identifier>
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        <date>2023-07</date>
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%0 Conference Proceedings
%T Reliable and Interpretable Drift Detection in Streams of Short Texts
%A Rabinovich, Ella
%A Vetzler, Matan
%A Ackerman, Samuel
%A Anaby Tavor, Ateret
%Y Sitaram, Sunayana
%Y Beigman Klebanov, Beata
%Y Williams, Jason D.
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 5: Industry Track)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F rabinovich-etal-2023-reliable
%X Data drift is the change in model input data that is one of the key factors leading to machine learning models performance degradation over time. Monitoring drift helps detecting these issues and preventing their harmful consequences. Meaningful drift interpretation is a fundamental step towards effective re-training of the model. In this study we propose an end-to-end framework for reliable model-agnostic change-point detection and interpretation in large task-oriented dialog systems, proven effective in multiple customer deployments. We evaluate our approach and demonstrate its benefits with a novel variant of intent classification training dataset, simulating customer requests to a dialog system. We make the data publicly available.
%R 10.18653/v1/2023.acl-industry.42
%U https://aclanthology.org/2023.acl-industry.42
%U https://doi.org/10.18653/v1/2023.acl-industry.42
%P 438-446

Markdown (Informal)

[Reliable and Interpretable Drift Detection in Streams of Short Texts](https://aclanthology.org/2023.acl-industry.42) (Rabinovich et al., ACL 2023)

• Reliable and Interpretable Drift Detection in Streams of Short Texts (Rabinovich et al., ACL 2023)

ACL

• Ella Rabinovich, Matan Vetzler, Samuel Ackerman, and Ateret Anaby Tavor. 2023. Reliable and Interpretable Drift Detection in Streams of Short Texts. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 5: Industry Track), pages 438–446, Toronto, Canada. Association for Computational Linguistics.