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). |
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| Projects |
Idiap CRITERIA |
| Authors | |
| Added by: | [UNK] |
| Total mark: | 0 |
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