Learning Markup Language Model for Composite Relationships Extraction

Generative approaches to relational triplet extraction (RTE) developed on the sequence model have effectively handled monotonous relations. However, the nuanced complexities of composite relationships among entities significantly challenge their performance. In this paper, we adapt pre-trained language models (PLMs) to extract complex relations in RTE, for which we propose a novel learning framework, named MarkET. Specifically, MarkET uses markup language with a hierarchical syntax structure to concisely encode diverse relationships, which allows a PLM to seamlessly integrate the extraction of relational triplets with its advanced language-generating capabilities. To further ensure the validity of model outputs, MarkET trains the PLM through a two-stage curriculum, consisting of the bootstrap tasks of entity and relation recognition and the primary task RTE, each with incrementally increased difficulty along the training steps. We conduct extensive experiments on four public datasets, and our approach is approved superior to previous SOTA across diverse settings and metrics.