IDIAPers @ Causal News Corpus 2022: Efficient Causal Relation Identification Through a Prompt-based Few-shot Approach

TL;DR

This paper presents a prompt-based few-shot learning approach for causal relation identification in news texts, achieving high accuracy with minimal training data.

Contribution

It introduces a novel prompt-based fine-tuning method for language models that performs well in causal relation detection with limited annotated examples.

Findings

Achieved second-best precision of 0.82 with only 15.7% of data.

Demonstrated effectiveness of prompt-based few-shot learning in CRI.

Close performance to the top team with minimal data.

Abstract

In this paper, we describe our participation in the subtask 1 of CASE-2022, Event Causality Identification with Casual News Corpus. We address the Causal Relation Identification (CRI) task by exploiting a set of simple yet complementary techniques for fine-tuning language models (LMs) on a small number of annotated examples (i.e., a few-shot configuration). We follow a prompt-based prediction approach for fine-tuning LMs in which the CRI task is treated as a masked language modeling problem (MLM). This approach allows LMs natively pre-trained on MLM problems to directly generate textual responses to CRI-specific prompts. We compare the performance of this method against ensemble techniques trained on the entire dataset. Our best-performing submission was fine-tuned with only 256 instances per class, 15.7% of the all available data, and yet obtained the second-best precision (0.82), third-best accuracy (0.82), and an F1-score (0.85) very close to what was reported by the winner team (0.86).