Existing approaches on zero-shot event detection usually train models on datasets annotated with known event types, and prompt them with unseen event definitions. These approaches yield sporadic successes, yet generally fall short of expectations. In this work, we aim to improve zero-shot event detection by training models to better follow event definitions. We hypothesize that a diverse set of event types and definitions are the key for models to learn to follow event definitions while existing event extraction datasets focus on annotating many high-quality examples for a few event types. To verify our hypothesis, we construct an automatically generated Diverse Event Definition (DivED) dataset and conduct comparative studies. Our experiments reveal that a large number of event types (200) and diverse event definitions can significantly boost event extraction performance; on the other hand, the performance does not scale with over ten examples per event type. Beyond scaling, we incorporate event ontology information and hard-negative samples during training, further boosting the performance. Based on these findings, we fine-tuned a LLaMA-2-7B model on our DivED dataset, yielding performance that surpasses SOTA large language models like GPT-3.5 across three open benchmarks on zero-shot event detection.

翻译：现有零样本事件检测方法通常使用已知事件类型标注的数据集训练模型，并通过未见事件定义进行提示。这些方法虽偶有成功，但普遍未达预期。本研究旨在通过训练模型更好地遵循事件定义来改进零样本事件检测。我们假设丰富的事件类型和定义多样性是模型学习遵循事件定义的关键，而现有事件抽取数据集侧重于为少数事件类型标注大量高质量示例。为验证这一假设，我们构建了自动生成的多样化事件定义（DivED）数据集并开展比较研究。实验表明：大量事件类型（200种）和多样化事件定义能显著提升事件抽取性能；相反，每个事件类型超过十个示例时性能不再随规模提升。除扩展规模外，我们在训练过程中融入事件本体信息与困难负样本，进一步提升了性能。基于这些发现，我们在DivED数据集上微调了LLaMA-2-7B模型，其在三个开放基准的零样本事件检测任务中均超越了GPT-3.5等顶尖大语言模型。