@inproceedings{mou-etal-2022-uninl,
title = "{U}ni{NL}: Aligning Representation Learning with Scoring Function for {OOD} Detection via Unified Neighborhood Learning",
author = "Mou, Yutao and
Wang, Pei and
He, Keqing and
Wu, Yanan and
Wang, Jingang and
Wu, Wei and
Xu, Weiran",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.494",
doi = "10.18653/v1/2022.emnlp-main.494",
pages = "7317--7325",
abstract = "Detecting out-of-domain (OOD) intents from user queries is essential for avoiding wrong operations in task-oriented dialogue systems. The key challenge is how to distinguish in-domain (IND) and OOD intents. Previous methods ignore the alignment between representation learning and scoring function, limiting the OOD detection performance. In this paper, we propose a unified neighborhood learning framework (UniNL) to detect OOD intents. Specifically, we design a KNCL objective for representation learning, and introduce a KNN-based scoring function for OOD detection. We aim to align representation learning with scoring function. Experiments and analysis on two benchmark datasets show the effectiveness of our method.",
}
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<abstract>Detecting out-of-domain (OOD) intents from user queries is essential for avoiding wrong operations in task-oriented dialogue systems. The key challenge is how to distinguish in-domain (IND) and OOD intents. Previous methods ignore the alignment between representation learning and scoring function, limiting the OOD detection performance. In this paper, we propose a unified neighborhood learning framework (UniNL) to detect OOD intents. Specifically, we design a KNCL objective for representation learning, and introduce a KNN-based scoring function for OOD detection. We aim to align representation learning with scoring function. Experiments and analysis on two benchmark datasets show the effectiveness of our method.</abstract>
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%0 Conference Proceedings
%T UniNL: Aligning Representation Learning with Scoring Function for OOD Detection via Unified Neighborhood Learning
%A Mou, Yutao
%A Wang, Pei
%A He, Keqing
%A Wu, Yanan
%A Wang, Jingang
%A Wu, Wei
%A Xu, Weiran
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F mou-etal-2022-uninl
%X Detecting out-of-domain (OOD) intents from user queries is essential for avoiding wrong operations in task-oriented dialogue systems. The key challenge is how to distinguish in-domain (IND) and OOD intents. Previous methods ignore the alignment between representation learning and scoring function, limiting the OOD detection performance. In this paper, we propose a unified neighborhood learning framework (UniNL) to detect OOD intents. Specifically, we design a KNCL objective for representation learning, and introduce a KNN-based scoring function for OOD detection. We aim to align representation learning with scoring function. Experiments and analysis on two benchmark datasets show the effectiveness of our method.
%R 10.18653/v1/2022.emnlp-main.494
%U https://aclanthology.org/2022.emnlp-main.494
%U https://doi.org/10.18653/v1/2022.emnlp-main.494
%P 7317-7325
Markdown (Informal)
[UniNL: Aligning Representation Learning with Scoring Function for OOD Detection via Unified Neighborhood Learning](https://aclanthology.org/2022.emnlp-main.494) (Mou et al., EMNLP 2022)
ACL