CsFEVER and CTKFacts: acquiring Czech data for fact verification

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3ABK7DBERX" target="_blank" >RIV/00216208:11320/23:BK7DBERX - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1007/s10579-023-09654-3" target="_blank" >https://doi.org/10.1007/s10579-023-09654-3</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10579-023-09654-3" target="_blank" >10.1007/s10579-023-09654-3</a>

Result language

angličtina

Original language name

CsFEVER and CTKFacts: acquiring Czech data for fact verification

Original language description

"In this paper, we examine several methods of acquiring Czech data for automated fact-checking, which is a task commonly modeled as a classification of textual claim veracity w.r.t. a corpus of trusted ground truths. We attempt to collect sets of data in form of a factual claim, evidence within the ground truth corpus, and its veracity label (supported, refuted or not enough info). As a first attempt, we generate a Czech version of the large-scale FEVER dataset built on top of Wikipedia corpus. We take a hybrid approach of machine translation and document alignment; the approach and the tools we provide can be easily applied to other languages. We discuss its weaknesses, propose a future strategy for their mitigation and publish the 127k resulting translations, as well as a version of such dataset reliably applicable for the Natural Language Inference task—the CsFEVER-NLI. Furthermore, we collect a novel dataset of 3,097 claims, which is annotated using the corpus of 2.2 M articles of Czech News Agency. We present an extended dataset annotation methodology based on the FEVER approach, and, as the underlying corpus is proprietary, we also publish a standalone version of the dataset for the task of Natural Language Inference we call CTKFactsNLI. We analyze both acquired datasets for spurious cues—annotation patterns leading to model overfitting. CTKFacts is further examined for inter-annotator agreement, thoroughly cleaned, and a typology of common annotator errors is extracted. Finally, we provide baseline models for all stages of the fact-checking pipeline and publish the NLI datasets, as well as our annotation platform and other experimental data."

Czech name

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Czech description

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Type

J_ost - Miscellaneous article in a specialist periodical

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)

Project

—

Continuities

—

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

"Language Resources and Evaluation"

ISSN

1574-0218

e-ISSN

—

Volume of the periodical

57

Issue of the periodical within the volume

4

Country of publishing house

US - UNITED STATES

Number of pages

35

Pages from-to

1571-1605

UT code for WoS article

000980799100007

EID of the result in the Scopus database

2-s2.0-85158137690