Cross-document event coreference resolution (CDECR) involves clustering event
mentions across multiple documents that refer to the same real-world events.
Existing approaches utilize fine-tuning of small language models (SLMs) like
BERT to address the compatibility among the contexts of event mentions.
However, due to the complexity and diversity of contexts, these models are
prone to learning simple co-occurrences. Recently, large language models (LLMs)
like ChatGPT have demonstrated impressive contextual understanding, yet they
encounter challenges in adapting to specific information extraction (IE) tasks.
In this paper, we propose a collaborative approach for CDECR, leveraging the
capabilities of both a universally capable LLM and a task-specific SLM. The
collaborative strategy begins with the LLM accurately and comprehensively
summarizing events through prompting. Then, the SLM refines its learning of
event representations based on these insights during fine-tuning. Experimental
results demonstrate that our approach surpasses the performance of both the
large and small language models individually, forming a complementary
advantage. Across various datasets, our approach achieves state-of-the-art
performance, underscoring its effectiveness in diverse scenarios.

利用大型语言模型和小型语言模型协作的方法在跨文档事件共指解析中取得了最好的性能，并且在不同的数据集中都表现出了最先进的性能，强调了其在多样场景中的有效性。

协同事件理解：基于大型语言模型的跨文档事件共指消解的协同方法

Synergetic Event Understanding: A Collaborative Approach to  Cross-Document Event Coreference Resolution with Large Language Models

Event linking connects event mentions in text with relevant nodes in a
knowledge base (KB). Prior research in event linking has mainly borrowed
methods from entity linking, overlooking the distinct features of events.
Compared to the extensively explored entity linking task, events have more
complex structures and can be more effectively distinguished by examining their
associated arguments. Moreover, the information-rich nature of events leads to
the scarcity of event KBs. This emphasizes the need for event linking models to
identify and classify event mentions not in the KB as ``out-of-KB,'' an area
that has received limited attention. In this work, we tackle these challenges
by introducing an argument-aware approach. First, we improve event linking
models by augmenting input text with tagged event argument information,
facilitating the recognition of key information about event mentions.
Subsequently, to help the model handle ``out-of-KB'' scenarios, we synthesize
out-of-KB training examples from in-KB instances through controlled
manipulation of event arguments. Our experiment across two test datasets showed
significant enhancements in both in-KB and out-of-KB scenarios, with a notable
22% improvement in out-of-KB evaluations.

事件链接通过识别事件提及与知识库中相关的节点相连。该研究提出了一种关注事件参数的方法，以解决事件链接中的若干挑战，包括事件参数信息的识别和处理知识库外事件提及的问题。在两个测试数据集上进行的实验证明，该方法在处理知识库内外的情况下都取得了显著的改进，其中处理知识库外事件提及的效果提高了 22%。

事件链接的论证感知方法

Argument-Aware Approach To Event Linking

Event Causality Identification (ECI) aims at determining whether there is a
causal relation between two event mentions. Conventional prompt learning
designs a prompt template to first predict an answer word and then maps it to
the final decision. Unlike conventional prompts, we argue that predicting an
answer word may not be a necessary prerequisite for the ECI task. Instead, we
can first make a deterministic assumption on the existence of causal relation
between two events and then evaluate its rationality to either accept or reject
the assumption. The design motivation is to try the most utilization of the
encyclopedia-like knowledge embedded in a pre-trained language model. In light
of such considerations, we propose a deterministic assumption prompt learning
model, called DAPrompt, for the ECI task. In particular, we design a simple
deterministic assumption template concatenating with the input event pair,
which includes two masks as predicted events' tokens. We use the probabilities
of predicted events to evaluate the assumption rationality for the final event
causality decision. Experiments on the EventStoryLine corpus and
Causal-TimeBank corpus validate our design objective in terms of significant
performance improvements over the state-of-the-art algorithms.

事件因果识别（ECI）是确定两个事件提及之间是否存在因果关系的任务。本研究提出了一种确定性假设提示学习模型（DAPrompt），通过评估事件间因果关系的假设合理性，显著改进了现有算法在事件因果识别任务中的性能。