Event extraction aims to recognize pre-defined event triggers and arguments
from texts, which suffer from the lack of high-quality annotations. In most NLP
applications, involving a large scale of synthetic training data is a practical
and effective approach to alleviate the problem of data scarcity. However, when
applying to the task of event extraction, recent data augmentation methods
often neglect the problem of grammatical incorrectness, structure misalignment,
and semantic drifting, leading to unsatisfactory performances. In order to
solve these problems, we propose a denoised structure-to-text augmentation
framework for event extraction DAEE, which generates additional training data
through the knowledge-based structure-to-text generation model and selects the
effective subset from the generated data iteratively with a deep reinforcement
learning agent. Experimental results on several datasets demonstrate that the
proposed method generates more diverse text representations for event
extraction and achieves comparable results with the state-of-the-art.

本文提出了一种通过知识基础结构生成模型和深度强化学习代理程序迭代地从中选择有效子集的去噪结构生成增强框架（DAEE），以解决事件提取任务中语法不正确、结构不对齐和语义偏差等问题，并在多个数据集上进行试验，结果表明该方法生成了更多元化的事件提取文本表示，并取得了与最先进技术相当的效果。