DAPrompt: Deterministic Assumption Prompt Learning for Event Causality Identification

TL;DR

DAPrompt introduces a novel deterministic assumption approach for event causality identification, leveraging pre-trained language models' knowledge to improve accuracy without relying on answer word prediction.

Contribution

The paper proposes DAPrompt, a new prompt learning method that evaluates the rationality of deterministic causal assumptions, enhancing event causality detection performance.

Findings

Significant performance improvements over state-of-the-art methods.

Effective utilization of pre-trained language models' knowledge.

Validated on EventStoryLine and Causal-TimeBank datasets.

Abstract

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.