Detail výsledku
IDIAPers @ Causal News Corpus 2022: Efficient Causal Relation Identification Through a Prompt-based Few-shot Approach
Fajčík Martin, Ing., Ph.D., UPGM (FIT)
Smrž Pavel, doc. RNDr., Ph.D., UPGM (FIT)
Motlíček Petr, doc. Ing., Ph.D., UPGM (FIT)
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).
few-shot learning, classifier, causal relation identification, causal event identification, ensembling
@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/"
}