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。

DAPrompt: 确定性假设提示学习用于事件因果关系识别

DAPrompt: Deterministic Assumption Prompt Learning for Event Causality  Identification

Event Causality Identification (ECI) aims to identify causal relations
between events in unstructured texts. This is a very challenging task, because
causal relations are usually expressed by implicit associations between events.
Existing methods usually capture such associations by directly modeling the
texts with pre-trained language models, which underestimate two kinds of
semantic structures vital to the ECI task, namely, event-centric structure and
event-associated structure. The former includes important semantic elements
related to the events to describe them more precisely, while the latter
contains semantic paths between two events to provide possible supports for
ECI. In this paper, we study the implicit associations between events by
modeling the above explicit semantic structures, and propose a Semantic
Structure Integration model (SemSIn). It utilizes a GNN-based event aggregator
to integrate the event-centric structure information, and employs an LSTM-based
path aggregator to capture the event-associated structure information between
two events. Experimental results on three widely used datasets show that SemSIn
achieves significant improvements over baseline methods.

本文提出了一种名为 SemSIn 的模型，通过模拟事件中心结构和事件关联结构之间的语义关系，更准确地识别事件之间的因果关系，并在三个广泛使用的数据集上取得了显着的性能提升。