DAPrompt: Deterministic Assumption Prompt Learning for Event Causality
  Identification
Wei Xiang Chuanhong Zhan Bang Wang
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.