Boosting Event Extraction with Denoised Structure-to-Text Augmentation
bo wang Heyan Huang Xiaochi Wei Ge Shi Xiao Liu Chong Feng Tong Zhou Shuaiqiang Wang Dawei Yin
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.