Publication Details
IDIAPers @ Causal News Corpus 2022: Efficient Causal Relation Identification Through a Prompt-based Few-shot Approach
Fajčík Martin, Ing., Ph.D. (DCGM)
Smrž Pavel, doc. RNDr., Ph.D. (DCGM)
Motlíček Petr, doc. Ing., Ph.D. (DCGM)
few-shot learning, classifier, causal relation identification, causal event identification, ensembling
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).
@inproceedings{BUT185127,
author="Sergio {Burdisso} and Martin {Fajčík} and Pavel {Smrž} and Petr {Motlíček}",
title="IDIAPers @ Causal News Corpus 2022: Efficient Causal Relation Identification Through a Prompt-based Few-shot Approach",
booktitle="Proceedings of the 5th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2022)",
year="2022",
pages="61--69",
publisher="Association for Computational Linguistics",
address="Abu Dhabi",
doi="10.18653/v1/2022.case-1.9",
isbn="978-1-959429-05-0",
url="https://aclanthology.org/2022.case-1.9/"
}