ArgFuse: A Weakly-Supervised Framework for Document-Level Event Argument Aggregation

TL;DR

ArgFuse introduces a weakly-supervised framework for aggregating document-level event arguments, effectively filtering redundant information to generate precise information frames from lengthy texts, especially in low-resource settings.

Contribution

The paper presents the first baseline for document-level event argument aggregation using a weakly-supervised, extractive algorithm with active learning strategies.

Findings

Developed a new dataset with 131 annotated document information frames.

Proposed an efficient extractive algorithm with multiple sieves and active learning.

Achieved baseline results for document-level argument aggregation in English.

Abstract

Most of the existing information extraction frameworks (Wadden et al., 2019; Veysehet al., 2020) focus on sentence-level tasks and are hardly able to capture the consolidated information from a given document. In our endeavour to generate precise document-level information frames from lengthy textual records, we introduce the task of Information Aggregation or Argument Aggregation. More specifically, our aim is to filter irrelevant and redundant argument mentions that were extracted at a sentence level and render a document level information frame. Majority of the existing works have been observed to resolve related tasks of document-level event argument extraction (Yang et al., 2018a; Zheng et al., 2019a) and salient entity identification (Jain et al.,2020) using supervised techniques. To remove dependency from large amounts of labelled data, we explore the task of information aggregation using weakly-supervised techniques. In particular, we present an extractive algorithm with multiple sieves which adopts active learning strategies to work efficiently in low-resource settings. For this task, we have annotated our own test dataset comprising of 131 document information frames and have released the code and dataset to further research prospects in this new domain. To the best of our knowledge, we are the first to establish baseline results for this task in English. Our data and code are publicly available at https://github.com/DebanjanaKar/ArgFuse.