From Discourse to Narrative: Knowledge Projection for Event Relation Extraction (2021)

Abstract: Current event-centric knowledge graphs highly rely on explicit connectives to mine relations between events. Unfortunately, due to the sparsity of connectives, these methods severely undermine the coverage of EventKGs. The lack of high-quality labelled corpora further exacerbates that problem. In this paper, we propose a knowledge projection paradigm for event relation extraction: projecting discourse knowledge to narratives by exploiting the commonalities between them. Specifically, we propose Multi-tier Knowledge Projection Network (MKPNet), which can leverage multi-tier discourse knowledge effectively for event relation extraction. In this way, the labelled data requirement is significantly reduced, and implicit event relations can be effectively extracted. Intrinsic experimental results show that MKPNet achieves the new state-of-the-art performance and extrinsic experimental results verify the value of the extracted event relations.

## introduction event-centric knowledge graphs (eventkgs) model the narratives of the world by representing events and identifying relations between them, which are critical for machine understanding and can benefit many downstream tasks, such as question answering @xcite , news reading @xcite , commonsense knowledge acquisition @xcite and so on. recently, semi-automatically constructing even-tkgs have gained much attention @xcite @xcite . these methods extract event knowledge from massive raw corpora with or without little human intervention, which makes them scalable solutions to build large-scale ## multi-tier knowledge projection network for event relation extraction in this section, we describe how to learn an effective event relation extractor by projecting resourcerich discourse knowledge to the resource-poor narrative task. specifically, we propose multi-tier knowledge projection network (mkpnet) which can effectively leverage multi-tier discourse knowledge for implicit event relation extraction. figure 2 shows an overview of mkpnet, which uses token adaptor, semantic adaptor and coarse category adaptor to fully exploit discourse knowledge at different levels. in the following, we first describe the neural architecture of mkpnet and then describe the details of three adaptors. ## experiments we conduct intrinsic experiments on aser @xcite to assess the effectiveness of the proposed mkpnet, and extrinsic experiments on wsc @xcite to verify the value of the extracted event relations. ## conclusions in this paper, we propose a knowledge projection paradigm for event relation extraction and multitier knowledge projection network (mkpnet) is designed to leverage multi-tier discourse knowledge. by effectively projecting knowledge from discourses to narratives, mkpnet achieves the new state-of-the-art event relation extraction performance, and extrinsic experimental results verify the value of the extracted event relations. for future work, we want to design new data-efficient algorithms to learn effective models using low-quality and heterogeneous knowledge.