Publication Details

IDIAPers @ Causal News Corpus 2022: Extracting Cause-Effect-Signal Triplets via Pre-trained Autoregressive Language Model

FAJČÍK Martin, SMRŽ Pavel, MOTLÍČEK Petr and BURDISSO Sergio et al. IDIAPers @ Causal News Corpus 2022: Extracting Cause-Effect-Signal Triplets via Pre-trained Autoregressive Language Model. In: Proceedings of the 5th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2022). Abu Dhabi: Association for Computational Linguistics, 2022, pp. 70-78. ISBN 978-1-959429-05-0. Available from: https://aclanthology.org/2022.case-1.10/
Czech title
IDIAPers @ Causal News Corpus 2022: Extrahování tripletů příčina-účinek-signál prostřednictvím předtrénovaného autoregresivního jazykového modelu
Type
conference paper
Language
english
Authors
URL
Keywords

causal event extraction, causal event, cause, effect, signal, newsmedia

Abstract

In this paper, we describe our shared task submissions for Subtask 2 in CASE-2022, Event Causality Identification with Casual News Corpus. The challenge focused on the automatic detection of all cause-effect-signal spans present in the sentence from news-media. We detect cause-effect-signal spans in a sentence using T5 --- a pre-trained autoregressive language model. We iteratively identify all cause-effect-signal span triplets, always conditioning the prediction of the next triplet on the previously predicted ones. To predict the triplet itself, we consider different causal relationships such as cause->effect->signal. Each triplet component is generated via a language model conditioned on the sentence, the previous parts of the current triplet, and previously predicted triplets. Despite training on an extremely small dataset of 160 samples, our approach achieved competitive performance, being placed second in the competition. Furthermore, we show that assuming either cause->effect or effect->cause order achieves similar results.

Published
2022
Pages
70-78
Proceedings
Proceedings of the 5th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2022)
Conference
Conference on Empirical Methods in Natural Language Processing, Abu Dhabi, AE
ISBN
978-1-959429-05-0
Publisher
Association for Computational Linguistics
Place
Abu Dhabi, AE
DOI
EID Scopus
BibTeX
@INPROCEEDINGS{FITPUB12838,
   author = "Martin Faj\v{c}\'{i}k and Pavel Smr\v{z} and Petr Motl\'{i}\v{c}ek and Sergio Burdisso and et al.",
   title = "IDIAPers @ Causal News Corpus 2022: Extracting Cause-Effect-Signal Triplets via Pre-trained Autoregressive Language Model",
   pages = "70--78",
   booktitle = "Proceedings of the 5th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2022)",
   year = 2022,
   location = "Abu Dhabi, AE",
   publisher = "Association for Computational Linguistics",
   ISBN = "978-1-959429-05-0",
   doi = "10.18653/v1/2022.case-1.10",
   language = "english",
   url = "https://www.fit.vut.cz/research/publication/12838"
}
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