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