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.

通过研究零样本事件检测中的事件类型和定义，提出了使用多样化的事件类型和定义来提高模型遵循事件定义的方法，并构建了一个自动生成的多样化事件定义（DivED）数据集进行实证研究，结果显示，大量的事件类型和多样化的事件定义可以显著提升事件提取性能，同时在训练中引入事件本体信息和困难负样本可以进一步提高性能，基于这些发现，在 DivED 数据集上微调 LLaMA-2-7B 模型，在三个零样本事件检测基准上取得了超越 GPT-3.5 等最新大语言模型的性能。

改进零样本事件检测的事件定义

Improving Event Definition Following For Zero-Shot Event Detection

We propose a new zero-shot Event Detection method by Multi-modal
Distributional Semantic embedding of videos. Our model embeds object and action
concepts as well as other available modalities from videos into a
distributional semantic space. To our knowledge, this is the first Zero-Shot
event detection model that is built on top of distributional semantics and
extends it in the following directions: (a) semantic embedding of multimodal
information in videos (with focus on the visual modalities), (b) automatically
determining relevance of concepts/attributes to a free text query, which could
be useful for other applications, and (c) retrieving videos by free text event
query (e.g., "changing a vehicle tire") based on their content. We embed videos
into a distributional semantic space and then measure the similarity between
videos and the event query in a free text form. We validated our method on the
large TRECVID MED (Multimedia Event Detection) challenge. Using only the event
title as a query, our method outperformed the state-of-the-art that uses big
descriptions from 12.6% to 13.5% with MAP metric and 0.73 to 0.83 with ROC-AUC
metric. It is also an order of magnitude faster.

本文提出了一种基于多模态分布语义嵌入的零样本事件检测方法，以视频为载体，将对象和动作等概念以及其他可用形式的多模态信息嵌入到分布语义空间中，实现了自由文本事件查询、概念属性的自动确定、视频检索等功能，在 TRECVID MED 大规模多媒体事件检测挑战中验证，有效性和速度均优于目前最先进的技术水平。