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- W4385570115 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." @default.
- W4385570115 created "2023-08-05" @default.
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- W4385570115 date "2023-01-01" @default.
- W4385570115 modified "2023-09-24" @default.
- W4385570115 title "Two Birds One Stone: Dynamic Ensemble for OOD Intent Classification" @default.
- W4385570115 doi "https://doi.org/10.18653/v1/2023.acl-long.595" @default.
- W4385570115 hasPublicationYear "2023" @default.
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