A Boundary Token Graph for Zero-Shot Relation Triplet Extraction Involving Discontinuous Entities - Lyu, Kailun; Li, Zehan; Zhang, Fu; Cheng, Jingwei (Proceedings of the AAAI Conference on Artificial Intelligence, 2026)
Zero-Shot Relation Triplet Extraction (ZSRTE) aims to extract head-tail entity pairs and their corresponding relations from sentences, where the relations available during inference are not seen during training. Existing methods typically assume that entities are continuous; however, in practice, entities can be discontinuous, which poses challenges to these approaches. To address this issue, we are the first to discuss and study the ZSRTE task involving discontinuous entities, and propose an innovative BoG framework, which is based on our proposed Boundary Token Graph structure. This method first predicts and adds edges between boundary tokens of (dis)continuous entities to construct a token graph, and then innovatively transforms the relation triplet extraction task into a process of finding paths in the graph. Additionally, we design a Boundary Token-Aware Prompt for each relation to further enhance the interaction between boundary tokens and relation semantics. Experimental results on four ZSRTE datasets—with or without discontinuous entities—consistently demonstrate that our method outperforms previous approaches, achieving state-of-the-art results.