Verbalizer or Classifier? A New Prompt Learning Model for Event Causality Identification

Abstract

Existing models for event causality identification (ECI) are mainly based on pre-training and fine-tuning paradigm, which are facing the problem of data lacking. Recent works have suggested that prompt learning has remarkable superiority in low-data scenario. The core idea of prompt learning is to insert text pieces, i.e., template, to the input and transform an ECI problem into a masked language modeling problem, in which a crucial part, dubbed as verbalizer, is to construct the projection between label space and word space. Previous works construct verbalizer through handcrafted or auto-searched way, which does not fully take the information of the MASK token. In this work, we propose a novel prompt learning model based on a classifier for ECI task. To be specific, we employ a multilayer perceptron (MLP) based classifier rather than verbalizer to incorporate full information provided at [MASK] token, and then project the output of MLP to the label space for ECI task. Experimental results on two benchmarks EventStoryLine and Causal-TimeBank demonstrate that our model has a huge superiority than previous models.