ParsFEVER: a Dataset for Farsi Fact Extraction and Verification

Majid Zarharan, Mahsa Ghaderan, Amin Pourdabiri, Zahra Sayedi, Behrouz Minaei-Bidgoli, Sauleh Eetemadi, Mohammad Taher Pilehvar


Abstract
Training and evaluation of automatic fact extraction and verification techniques require large amounts of annotated data which might not be available for low-resource languages. This paper presents ParsFEVER: the first publicly available Farsi dataset for fact extraction and verification. We adopt the construction procedure of the standard English dataset for the task, i.e., FEVER, and improve it for the case of low-resource languages. Specifically, claims are extracted from sentences that are carefully selected to be more informative. The dataset comprises nearly 23K manually-annotated claims. Over 65% of the claims in ParsFEVER are many-hop (require evidence from multiple sources), making the dataset a challenging benchmark (only 13% of the claims in FEVER are many-hop). Also, despite having a smaller training set (around one-ninth of that in Fever), a model trained on ParsFEVER attains similar downstream performance, indicating the quality of the dataset. We release the dataset and the annotation guidelines at https://github.com/Zarharan/ParsFEVER.
Anthology ID:
2021.starsem-1.9
Volume:
Proceedings of *SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics
Month:
August
Year:
2021
Address:
Online
Venues:
*SEM | ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
99–104
Language:
URL:
https://aclanthology.org/2021.starsem-1.9
DOI:
10.18653/v1/2021.starsem-1.9
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.starsem-1.9.pdf
Optional supplementary material:
 2021.starsem-1.9.OptionalSupplementaryMaterial.zip
Code
 zarharan/parsfever
Data
FEVERHoVerLIAR