IDIAPers @ Causal News Corpus 2022: Extracting Cause-Effect-Signal Triplets via Pre-trained Autoregressive Language Model
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F22%3APU149353" target="_blank" >RIV/00216305:26230/22:PU149353 - isvavai.cz</a>
Result on the web
<a href="https://aclanthology.org/2022.case-1.10/" target="_blank" >https://aclanthology.org/2022.case-1.10/</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.18653/v1/2022.case-1.10" target="_blank" >10.18653/v1/2022.case-1.10</a>
Alternative languages
Result language
angličtina
Original language name
IDIAPers @ Causal News Corpus 2022: Extracting Cause-Effect-Signal Triplets via Pre-trained Autoregressive Language Model
Original language description
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.
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
70-78
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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