Novel Relation Detection: Discovering Unknown Relation Types via Multi-Strategy Self-Supervised Learning

Qingbin Liu, Yin Kung, Yanchao Hao, Dianbo Sui, Siyuan Cheng, Xi Chen, Ningyu Zhang, Jiaoyan Chen

Submission Type: Regular Long Paper

Submission Track: Information Extraction

Keywords: Relation Detection, Out-of-Scope Detection, Self-Supervised Learning

TL;DR: Discovering Unknown Relation Types via M3S

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.

Submission Number: 1236