Targeted Augmentation for Low-Resource Event Extraction (2024)

Abstract: Addressing the challenge of low-resource information extraction remains an ongoing issue due to the inherent information scarcity within limited training examples. Existing data augmentation methods, considered potential solutions, struggle to strike a balance between weak augmentation (e.g., synonym augmentation) and drastic augmentation (e.g., conditional generation without proper guidance). This paper introduces a novel paradigm that employs targeted augmentation and back validation to produce augmented examples with enhanced diversity, polarity, accuracy, and coherence. Extensive experimental results demonstrate the effectiveness of the proposed paradigm. Furthermore, identified limitations are discussed, shedding light on areas for future improvement.

## introduction event extraction (ee) @xcite @xcite is the task of identifying and categorizing event mentions in natural language text. while supervised methods deliver impressive performance, they depend heavily on extensive manual annotations @xcite @xcite @xcite . generalizing these approaches to low-resource learning setting poses challenges @xcite @xcite @xcite @xcite . data augmentation is one direction for efficiently addressing the low-resource event extraction problem. however, it's remained unexplored what data augmentation strategies are the best for lowresource event extraction given its unique challenges. previous studies show that weak augmentations, such as synonym augmentation @xcite or through back translation @xcite , contribute minimally to distribution enrichment, while drastic augmentations can leading to misguided acquisitions @xcite . drastic augmentations usually undermine existing event structure, resulting in grammatical incorrectness, structure misalignment, or semantic drifting @xcite . in this work, we explore several dimensions for data augmentation, including diversity, polarity, accuracy, and coherence. our focus revolves around enhancing diversity in the context of targeted augmentation for low-resource event extraction (talor-ee). this involves enriching event structures with entities drawn from a targeted subset @xcite . simultaneously, we address the issue of polarity by not only generating positive event mentions based on actual occurrences but also incorporating negative event mentions, e.g., hypothetical event mentions @xcite . this approach is particularly valuable for overcoming limitations in generative event extraction models @xcite . to ensure both accuracy and coherence in our generated content, we introduce a back-and-forth validation module back-validation. the rationale behind this module is that an accurate generation should align with the given event structure, while coherent generation should seamlessly integrate with the same structure. our research encompasses a series of comprehensive experiments conducted across various lowresource learning scenarios, including zero-shot and few-shot learning settings. these experiments span different event extraction models. the outcomes of these experiments consistently highlight the effectiveness of targeted augmentation in lowresource event extraction. notably, among all the dimensions investigated, diversity emerges as the most crucial factor. additionally, we meticulously scrutinize the quality of the generated sentences, shedding light on the limitations inherent in the proposed framework. the contributions of this work are as follows: ## experiments we perform experiments on three public benchmark datasets, including ace05-e (automatic content extraction) 4 and ere (entity relation event) @xcite . to showcase the effectiveness of the proposed method under low resource settings, experiments are conducted under n way-kshot learning setting, where @xmath0, 10}, and @xmath1, 1, 5, 10}. ## conclusion in conclusion, this study proposes a new paradigm for tackling low-resource event extraction tasks. generation agents are employed to create a diverse training dataset for event structures enriched with domain-invariant entities. the generated examples undergo a thorough back-and-forth validation process to assess accuracy and coherence. our research encompasses extensive experiments in diverse low-resource learning scenarios, such as zero-shot and few-shot learning settings, across various event extraction models. the outcomes of these experiments highlight the effectiveness of the proposed framework. furthermore, our proposed methodology can inspire researchers from diverse domains to embrace a comparable paradigm or delve into the investigation of data augmentation methods as a means of enriching their training datasets. limitations talor-ee establishes a powerful starting point for advancing few-shot learning research, offering a flexible framework for framing new tasks through our proposed augmentation method. it encourages a systematic exploration of general and resilient enhancements for low-resource event extraction systems. however, augmenting non-event examples takes appropriate attention, as the proposed system may tend to predict additional event mentions. the absence of a clear distinction between an actual event and a non-event mention, due to the lack of a precise definition, underscores the need for appropriate action. we extend a warm invitation to future low-resource research endeavors and augmentation methods to delve into the structural aspects of event generation within a contrastive setting.