@inproceedings{mou-etal-2022-disentangled,
title = "Disentangled Knowledge Transfer for {OOD} Intent Discovery with Unified Contrastive Learning",
author = "Mou, Yutao and
He, Keqing and
Wu, Yanan and
Zeng, Zhiyuan and
Xu, Hong and
Jiang, Huixing and
Wu, Wei and
Xu, Weiran",
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-short.6",
doi = "10.18653/v1/2022.acl-short.6",
pages = "46--53",
abstract = "Discovering Out-of-Domain(OOD) intents is essential for developing new skills in a task-oriented dialogue system. The key challenge is how to transfer prior IND knowledge to OOD clustering. Different from existing work based on shared intent representation, we propose a novel disentangled knowledge transfer method via a unified multi-head contrastive learning framework. We aim to bridge the gap between IND pre-training and OOD clustering. Experiments and analysis on two benchmark datasets show the effectiveness of our method.",
}
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<abstract>Discovering Out-of-Domain(OOD) intents is essential for developing new skills in a task-oriented dialogue system. The key challenge is how to transfer prior IND knowledge to OOD clustering. Different from existing work based on shared intent representation, we propose a novel disentangled knowledge transfer method via a unified multi-head contrastive learning framework. We aim to bridge the gap between IND pre-training and OOD clustering. Experiments and analysis on two benchmark datasets show the effectiveness of our method.</abstract>
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%0 Conference Proceedings
%T Disentangled Knowledge Transfer for OOD Intent Discovery with Unified Contrastive Learning
%A Mou, Yutao
%A He, Keqing
%A Wu, Yanan
%A Zeng, Zhiyuan
%A Xu, Hong
%A Jiang, Huixing
%A Wu, Wei
%A Xu, Weiran
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F mou-etal-2022-disentangled
%X Discovering Out-of-Domain(OOD) intents is essential for developing new skills in a task-oriented dialogue system. The key challenge is how to transfer prior IND knowledge to OOD clustering. Different from existing work based on shared intent representation, we propose a novel disentangled knowledge transfer method via a unified multi-head contrastive learning framework. We aim to bridge the gap between IND pre-training and OOD clustering. Experiments and analysis on two benchmark datasets show the effectiveness of our method.
%R 10.18653/v1/2022.acl-short.6
%U https://aclanthology.org/2022.acl-short.6
%U https://doi.org/10.18653/v1/2022.acl-short.6
%P 46-53
Markdown (Informal)
[Disentangled Knowledge Transfer for OOD Intent Discovery with Unified Contrastive Learning](https://aclanthology.org/2022.acl-short.6) (Mou et al., ACL 2022)
ACL
- Yutao Mou, Keqing He, Yanan Wu, Zhiyuan Zeng, Hong Xu, Huixing Jiang, Wei Wu, and Weiran Xu. 2022. Disentangled Knowledge Transfer for OOD Intent Discovery with Unified Contrastive Learning. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 46–53, Dublin, Ireland. Association for Computational Linguistics.