Machine Reading Comprehension as Data Augmentation: A Case Study on Implicit Event Argument Extraction (2021)

Abstract: Implicit event argument extraction (EAE) is a crucial document-level information extraction task that aims to identify event arguments beyond the sentence level. Despite many efforts for this task, the lack of enough training data has long impeded the study. In this paper, we take a new perspective to address the data sparsity issue faced by implicit EAE, by bridging the task with machine reading comprehension (MRC). Particularly, we devise two data augmentation regimes via MRC, including: 1) implicit knowledge transfer, which enables knowledge transfer from other tasks, by building a unified training framework in the MRC formulation, and 2) explicit data augmentation, which can explicitly generate new training examples, by treating MRC models as an annotator. The extensive experiments have justified the effectiveness of our approach — it not only obtains state-of-the-art performance on two benchmarks, but also demonstrates superior results in a data-low scenario.

## introduction textual event descriptions may span over multiple sentences. implicit event argument extraction (eae) @xcite , a crucial task for event information extraction, aims to identify event arguments beyond the sentence level. for example, in a document describing an airstrikemissilestrike event (shown in figure 1 ), implicit eae requires a model to recognize a global event argument "syria", fulfilling the semantic role of place. note the argument is one-sentence away from the event trigger bombarding. one key challenge faced by implicit eae is data sparsity -owing to the complex interdependencies between triggers and arguments, it is expensive to label training data for the task. the existing datasets, which typically contain several dozens of documents, are too small to train a model for capturing regularities underlying how event argu-… this was indeed an is attack, rather than an accidental explosion. new satellite imagery appears to reveal extensive damage to [an airbase] victim in [central syria] place . ... q1: what is the victim in the attack event? ## implicit eae in mrc formulations we formulate implicit eae as follows: assume a document d contains a set of events e, each represented by an event trigger (e.g., bombarding in the previous example). the type of an event @xmath0, denoted by r e . for each semantic role @xmath1, implicit eae requires a model to find an event argument a, which is a textual span in d, resulting in a (r, a) pair foot_0 . different from previous methods addressing the task via sequence labeling @xcite or span ranking @xcite , we propose a new perspective based on mrc. ## data augmentation via mrc based on the above proxy of implicit eae and mrc, we devise two data augmentation regimes: implicit knowledge transfer ( § 4.1) and explicit data augmentation ( § 6.2).