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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: Idiap-RR
Citation: Burdisso_Idiap-RR-13-2022
Number: Idiap-RR-13-2022
Year: 2022
Month: 11
Institution: Idiap
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 trained only with 256 instances per class, a small portion of the entire dataset, and yet was able to obtain 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
Crossref by Burdisso_CASE@EMNLP2022_2022
Added by: [ADM]
Total mark: 0
  • Burdisso_Idiap-RR-13-2022.pdf (MD5: 2ee385822dc773967a25c5e6bb65e889)