Boosting Event Extraction with Denoised Structure-to-Text Augmentation (2023)

Abstract: 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.

## introduction event extraction is an essential yet challenging task for natural language understanding. given a piece of text, event extraction systems discover the event mentions and then recognize event triggers and their event arguments according to pre-defined event schema @xcite . as shown in figure 1 , the sentence "capture of the airport by american and british troops in a facility that has been airlifting american troops to baghdad." contains two events, a movement:transport event triggered by "airlifting" and a transaction:transfer-ownership event triggered by "capture". in the movement:transport event, three event roles are involved, i.e., artifact, destination, * corresponding author. transaction:transfer-ownership trigger beneficiary origin artifact original rule-based aug. ## method in this paper, we focus on generating the additional training set from structured event records for augmentation. previous augmentation methods usually have structure misalignment and grammatical incorrectness, and semantic drifting problems as mentioned in the introduction. instead, we introduce a policy-based rl strategy to select intact augmentation sentences. ## conclusion in this paper, we studied daee, the denoised structure-to-text augmentation framework for event extraction. the structure-to-text generation model with argument-aware loss is guided by the reinforcement learning agent to learn the task-specific information. meanwhile, the reinforcement learning agent selects effective samples from generated training data that are used to reinforce the event extraction performance. experimental results show that our model achieves competitive results with the sota on ace 2005, which is also a proven and effective generative data augmentation method for complex structure extraction.