@inproceedings{liu-etal-2023-novel,
title = "Novel Relation Detection: Discovering Unknown Relation Types via Multi-Strategy Self-Supervised Learning",
author = "Liu, Qingbin and
Kung, Yin and
Hao, Yanchao and
Sui, Dianbo and
Cheng, Siyuan and
Chen, Xi and
Zhang, Ningyu and
Chen, Jiaoyan",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.211",
doi = "10.18653/v1/2023.findings-emnlp.211",
pages = "3204--3214",
abstract = "Conventional approaches to relation extraction can only recognize predefined relation types. In the real world, new or out-of-scope relation types may keep challenging the deployed models. In this paper, we formalize such a challenging problem as Novel Relation Detection (NRD), which aims to discover potential new relation types based on training samples of known relations. To this end, we construct two NRD datasets and exhaustively investigate a variety of out-of-scope detection methods. We further propose an effective NRD method that utilizes multi-strategy self-supervised learning to handle the problem of shallow semantic similarity in the NRD task. Experimental results demonstrate the effectiveness of our method, which significantly outperforms previous state-of-the-art methods on both datasets.",
}
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<abstract>Conventional approaches to relation extraction can only recognize predefined relation types. In the real world, new or out-of-scope relation types may keep challenging the deployed models. In this paper, we formalize such a challenging problem as Novel Relation Detection (NRD), which aims to discover potential new relation types based on training samples of known relations. To this end, we construct two NRD datasets and exhaustively investigate a variety of out-of-scope detection methods. We further propose an effective NRD method that utilizes multi-strategy self-supervised learning to handle the problem of shallow semantic similarity in the NRD task. Experimental results demonstrate the effectiveness of our method, which significantly outperforms previous state-of-the-art methods on both datasets.</abstract>
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%0 Conference Proceedings
%T Novel Relation Detection: Discovering Unknown Relation Types via Multi-Strategy Self-Supervised Learning
%A Liu, Qingbin
%A Kung, Yin
%A Hao, Yanchao
%A Sui, Dianbo
%A Cheng, Siyuan
%A Chen, Xi
%A Zhang, Ningyu
%A Chen, Jiaoyan
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F liu-etal-2023-novel
%X Conventional approaches to relation extraction can only recognize predefined relation types. In the real world, new or out-of-scope relation types may keep challenging the deployed models. In this paper, we formalize such a challenging problem as Novel Relation Detection (NRD), which aims to discover potential new relation types based on training samples of known relations. To this end, we construct two NRD datasets and exhaustively investigate a variety of out-of-scope detection methods. We further propose an effective NRD method that utilizes multi-strategy self-supervised learning to handle the problem of shallow semantic similarity in the NRD task. Experimental results demonstrate the effectiveness of our method, which significantly outperforms previous state-of-the-art methods on both datasets.
%R 10.18653/v1/2023.findings-emnlp.211
%U https://aclanthology.org/2023.findings-emnlp.211
%U https://doi.org/10.18653/v1/2023.findings-emnlp.211
%P 3204-3214
Markdown (Informal)
[Novel Relation Detection: Discovering Unknown Relation Types via Multi-Strategy Self-Supervised Learning](https://aclanthology.org/2023.findings-emnlp.211) (Liu et al., Findings 2023)
ACL