@inproceedings{temiz-etal-2021-fact,
title = "A Fact Checking and Verification System for {FEVEROUS} Using a Zero-Shot Learning Approach",
author = {Temiz, Orkun and
K{\i}l{\i}{\c{c}}, {\"O}zg{\"u}n Ozan and
K{\i}z{\i}lda{\u{g}}, Arif Ozan and
Ta{\c{s}}kaya Temizel, Tu{\u{g}}ba},
editor = "Aly, Rami and
Christodoulopoulos, Christos and
Cocarascu, Oana and
Guo, Zhijiang and
Mittal, Arpit and
Schlichtkrull, Michael and
Thorne, James and
Vlachos, Andreas",
booktitle = "Proceedings of the Fourth Workshop on Fact Extraction and VERification (FEVER)",
month = nov,
year = "2021",
address = "Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.fever-1.13",
doi = "10.18653/v1/2021.fever-1.13",
pages = "113--120",
abstract = "In this paper, we propose a novel fact checking and verification system to check claims against Wikipedia content. Our system retrieves relevant Wikipedia pages using Anserini, uses BERT-large-cased question answering model to select correct evidence, and verifies claims using XLNET natural language inference model by comparing it with the evidence. Table cell evidence is obtained through looking for entity-matching cell values and TAPAS table question answering model. The pipeline utilizes zero-shot capabilities of existing models and all the models used in the pipeline requires no additional training. Our system got a FEVEROUS score of 0.06 and a label accuracy of 0.39 in FEVEROUS challenge.",
}
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<abstract>In this paper, we propose a novel fact checking and verification system to check claims against Wikipedia content. Our system retrieves relevant Wikipedia pages using Anserini, uses BERT-large-cased question answering model to select correct evidence, and verifies claims using XLNET natural language inference model by comparing it with the evidence. Table cell evidence is obtained through looking for entity-matching cell values and TAPAS table question answering model. The pipeline utilizes zero-shot capabilities of existing models and all the models used in the pipeline requires no additional training. Our system got a FEVEROUS score of 0.06 and a label accuracy of 0.39 in FEVEROUS challenge.</abstract>
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%0 Conference Proceedings
%T A Fact Checking and Verification System for FEVEROUS Using a Zero-Shot Learning Approach
%A Temiz, Orkun
%A Kılıç, Özgün Ozan
%A Kızıldağ, Arif Ozan
%A Taşkaya Temizel, Tuğba
%Y Aly, Rami
%Y Christodoulopoulos, Christos
%Y Cocarascu, Oana
%Y Guo, Zhijiang
%Y Mittal, Arpit
%Y Schlichtkrull, Michael
%Y Thorne, James
%Y Vlachos, Andreas
%S Proceedings of the Fourth Workshop on Fact Extraction and VERification (FEVER)
%D 2021
%8 November
%I Association for Computational Linguistics
%C Dominican Republic
%F temiz-etal-2021-fact
%X In this paper, we propose a novel fact checking and verification system to check claims against Wikipedia content. Our system retrieves relevant Wikipedia pages using Anserini, uses BERT-large-cased question answering model to select correct evidence, and verifies claims using XLNET natural language inference model by comparing it with the evidence. Table cell evidence is obtained through looking for entity-matching cell values and TAPAS table question answering model. The pipeline utilizes zero-shot capabilities of existing models and all the models used in the pipeline requires no additional training. Our system got a FEVEROUS score of 0.06 and a label accuracy of 0.39 in FEVEROUS challenge.
%R 10.18653/v1/2021.fever-1.13
%U https://aclanthology.org/2021.fever-1.13
%U https://doi.org/10.18653/v1/2021.fever-1.13
%P 113-120
Markdown (Informal)
[A Fact Checking and Verification System for FEVEROUS Using a Zero-Shot Learning Approach](https://aclanthology.org/2021.fever-1.13) (Temiz et al., FEVER 2021)
ACL