IDIAPers @ Causal News Corpus 2022: Efficient Causal Relation Identification Through a Prompt-based Few-shot Approach
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F22%3APU149354" target="_blank" >RIV/00216305:26230/22:PU149354 - isvavai.cz</a>
Result on the web
<a href="https://aclanthology.org/2022.case-1.9/" target="_blank" >https://aclanthology.org/2022.case-1.9/</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.18653/v1/2022.case-1.9" target="_blank" >10.18653/v1/2022.case-1.9</a>
Alternative languages
Result language
angličtina
Original language name
IDIAPers @ Causal News Corpus 2022: Efficient Causal Relation Identification Through a Prompt-based Few-shot Approach
Original language description
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).
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 5th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2022)
ISBN
978-1-959429-05-0
ISSN
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e-ISSN
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Number of pages
9
Pages from-to
61-69
Publisher name
Association for Computational Linguistics
Place of publication
Abu Dhabi
Event location
Abu Dhabi
Event date
Dec 7, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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