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        <title>Two Birds One Stone: Dynamic Ensemble for OOD Intent Classification</title>
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        <namePart type="given">Yunhua</namePart>
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        <namePart type="given">Jianqiang</namePart>
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    <name type="personal">
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    <name type="personal">
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    <abstract>Out-of-domain (OOD) intent classification is an active field of natural language understanding, which is of great practical significance for intelligent devices such as the Task-Oriented Dialogue System. It mainly contains two challenges: it requires the model to know what it knows and what it does not know. This paper investigates “overthinking” in the open-world scenario and its impact on OOD intent classification. Inspired by this, we propose a two-birds-one-stone method, which allows the model to decide whether to make a decision on OOD classification early during inference and can ensure accuracy and accelerate inference. At the same time, to adapt to the behavior of dynamic inference, we also propose a training method based on ensemble methods. In addition to bringing certain theoretical insights, we also conduct detailed experiments on three real-world intent datasets. Compared with the previous baselines, our method can not only improve inference speed, but also achieve significant performance improvements.</abstract>
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%0 Conference Proceedings
%T Two Birds One Stone: Dynamic Ensemble for OOD Intent Classification
%A Zhou, Yunhua
%A Yang, Jianqiang
%A Wang, Pengyu
%A Qiu, Xipeng
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F zhou-etal-2023-two
%X Out-of-domain (OOD) intent classification is an active field of natural language understanding, which is of great practical significance for intelligent devices such as the Task-Oriented Dialogue System. It mainly contains two challenges: it requires the model to know what it knows and what it does not know. This paper investigates “overthinking” in the open-world scenario and its impact on OOD intent classification. Inspired by this, we propose a two-birds-one-stone method, which allows the model to decide whether to make a decision on OOD classification early during inference and can ensure accuracy and accelerate inference. At the same time, to adapt to the behavior of dynamic inference, we also propose a training method based on ensemble methods. In addition to bringing certain theoretical insights, we also conduct detailed experiments on three real-world intent datasets. Compared with the previous baselines, our method can not only improve inference speed, but also achieve significant performance improvements.
%R 10.18653/v1/2023.acl-long.595
%U https://aclanthology.org/2023.acl-long.595/
%U https://doi.org/10.18653/v1/2023.acl-long.595
%P 10659-10673

Markdown (Informal)

[Two Birds One Stone: Dynamic Ensemble for OOD Intent Classification](https://aclanthology.org/2023.acl-long.595/) (Zhou et al., ACL 2023)

• Two Birds One Stone: Dynamic Ensemble for OOD Intent Classification (Zhou et al., ACL 2023)

ACL

• Yunhua Zhou, Jianqiang Yang, Pengyu Wang, and Xipeng Qiu. 2023. Two Birds One Stone: Dynamic Ensemble for OOD Intent Classification. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 10659–10673, Toronto, Canada. Association for Computational Linguistics.