MRC and Transfer Learning Framework for Document-Level Event Factuality Identification with Heterogeneous Spectral Attention Networks

Abstract

This paper concentrates on Document-level Event Factuality Identification (DEFI) that predicts event factuality values from the viewpoint of the document. At present, the shortcomings of previous studies are multi-fold, including data limitation and scarcity, coarsegrained interpretability without span-level factuality clues, no unified model for different datasets. This paper is devoted to address the above problems by building unified Machine Reading Comprehension (MRC) frameworks comprised of both span-extraction and multiple-choice styles, which exploit Heterogeneous Spectral Attention Networks (HSAN) with spectral networks and hypergraph attention networks as the fine-grained encoders, especially for span-level encoding. Moreover, we integrate Transfer Learning (TL) as cross-domain data augmentation to learn more span-level information from classical MRC datasets by source and target adapters. Experimental performance on ExDLEF corpus, which contains both English and Chinese documents, shows that our span-extraction MRC model is superior to several state-of-the-art baselines, and proves the effectiveness of transfer learning under MRC paradigms.