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。

通过句子简化改进零样本事件提取

Improving Zero-Shot Event Extraction via Sentence Simplification

We present a novel iterative, edit-based approach to unsupervised sentence
simplification. Our model is guided by a scoring function involving fluency,
simplicity, and meaning preservation. Then, we iteratively perform word and
phrase-level edits on the complex sentence. Compared with previous approaches,
our model does not require a parallel training set, but is more controllable
and interpretable. Experiments on Newsela and WikiLarge datasets show that our
approach is nearly as effective as state-of-the-art supervised approaches.

我们提出了一种新的迭代式基于编辑的无监督句子简化方法，其模型通过包括流畅性、简洁性和意义保持在内的评分函数进行指导，并在复杂句子上迭代执行单词和短语级别的编辑。与之前的方法相比，我们的模型不需要并行的训练集，但更可控和可解释。对 Newsela 和 WikiLarge 数据集的实验表明，我们的方法几乎与最先进的监督方法一样有效。