IDIAPers @ Causal News Corpus 2022: Efficient Causal Relation Identification Through a Prompt-based Few-shot Approach
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2022
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| author | Burdisso, Sergio Zuluaga-Gomez, Juan Villatoro-Tello, Esau Fajcik, Martin Singh, Muskaan Smrz, Pavel Motlicek, Petr |
| author_facet | Burdisso, Sergio Zuluaga-Gomez, Juan Villatoro-Tello, Esau Fajcik, Martin Singh, Muskaan Smrz, Pavel Motlicek, Petr |
| contents | 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). |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2209_03895 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | IDIAPers @ Causal News Corpus 2022: Efficient Causal Relation Identification Through a Prompt-based Few-shot Approach Burdisso, Sergio Zuluaga-Gomez, Juan Villatoro-Tello, Esau Fajcik, Martin Singh, Muskaan Smrz, Pavel Motlicek, Petr Computation and Language Artificial Intelligence Machine Learning 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). |
| title | IDIAPers @ Causal News Corpus 2022: Efficient Causal Relation Identification Through a Prompt-based Few-shot Approach |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2209.03895 |