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 [BibTeX] [Marc21]
IDIAPers @ Causal News Corpus 2022: Efficient Causal Relation Identification Through a Prompt-based Few-shot Approach
Type of publication: Conference paper
Citation: Burdisso_CASE@EMNLP2022_2022
Publication status: Accepted
Booktitle: The 5th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE @ EMNLP 2022)
Year: 2022
Month: December
Note: ACL Anthology (https://aclanthology.org/2022.case-1.9)
Crossref: Burdisso_Idiap-RR-13-2022:
URL: https://preview.aclanthology.o...
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).
Projects Idiap
Authors Burdisso, Sergio
Juan, Zuluaga-Gomez.
Fajcik, Martin
Singh, Muskaan
Smrz, Pavel
Motlicek, Petr
Added by: [UNK]
Total mark: 0