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). |
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| Projects |
Idiap |
| Authors | |
| Crossref by |
Burdisso_CASE@EMNLP2022_2022 |
| Added by: | [ADM] |
| Total mark: | 0 |
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