The success of sites such as ACLED and Our World in Data have demonstrated
the massive utility of extracting events in structured formats from large
volumes of textual data in the form of news, social media, blogs and discussion
forums. Event extraction can provide a window into ongoing geopolitical crises
and yield actionable intelligence. With the proliferation of large pretrained
language models, Machine Reading Comprehension (MRC) has emerged as a new
paradigm for event extraction in recent times. In this approach, event argument
extraction is framed as an extractive question-answering task. One of the key
advantages of the MRC-based approach is its ability to perform zero-shot
extraction. However, the problem of long-range dependencies, i.e., large
lexical distance between trigger and argument words and the difficulty of
processing syntactically complex sentences plague MRC-based approaches. In this
paper, we present a general approach to improve the performance of MRC-based
event extraction by performing unsupervised sentence simplification guided by
the MRC model itself. We evaluate our approach on the ICEWS geopolitical event
extraction dataset, with specific attention to `Actor' and `Target' argument
roles. We show how such context simplification can improve the performance of
MRC-based event extraction by more than 5% for actor extraction and more than
10% for target extraction.

本文提出一种通用方法，在 MRC 模型的指导下执行无监督的句子简化，以改善 MRC-based 事件提取的性能，并在 ICEWS 地缘政治事件提取数据集上进行评估，特别关注 'Actor' 和 'Target' 参数角色，结果显示上下文简化可以提高演员提取 5% 以上和目标提取 10% 以上的性能。