@inproceedings{funkquist-2021-combining,
title = "Combining sentence and table evidence to predict veracity of factual claims using {T}a{P}a{S} and {R}o{BERT}a",
author = "Funkquist, Martin",
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-1.10",
doi = "10.18653/v1/2021.fever-1.10",
pages = "92--100",
abstract = "This paper describes a method for retrieving evidence and predicting the veracity of factual claims, on the FEVEROUS dataset. The evidence consists of both sentences and table cells. The proposed method is part of the FEVER shared task. It uses similarity scores between TF-IDF vectors to retrieve the textual evidence and similarity scores between dense vectors created by fine-tuned TaPaS models for tabular evidence retrieval. The evidence is passed through a dense neural network to produce a veracity label. The FEVEROUS score for the proposed system is 0.126.",
}
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<abstract>This paper describes a method for retrieving evidence and predicting the veracity of factual claims, on the FEVEROUS dataset. The evidence consists of both sentences and table cells. The proposed method is part of the FEVER shared task. It uses similarity scores between TF-IDF vectors to retrieve the textual evidence and similarity scores between dense vectors created by fine-tuned TaPaS models for tabular evidence retrieval. The evidence is passed through a dense neural network to produce a veracity label. The FEVEROUS score for the proposed system is 0.126.</abstract>
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%0 Conference Proceedings
%T Combining sentence and table evidence to predict veracity of factual claims using TaPaS and RoBERTa
%A Funkquist, Martin
%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 funkquist-2021-combining
%X This paper describes a method for retrieving evidence and predicting the veracity of factual claims, on the FEVEROUS dataset. The evidence consists of both sentences and table cells. The proposed method is part of the FEVER shared task. It uses similarity scores between TF-IDF vectors to retrieve the textual evidence and similarity scores between dense vectors created by fine-tuned TaPaS models for tabular evidence retrieval. The evidence is passed through a dense neural network to produce a veracity label. The FEVEROUS score for the proposed system is 0.126.
%R 10.18653/v1/2021.fever-1.10
%U https://aclanthology.org/2021.fever-1.10
%U https://doi.org/10.18653/v1/2021.fever-1.10
%P 92-100
Markdown (Informal)
[Combining sentence and table evidence to predict veracity of factual claims using TaPaS and RoBERTa](https://aclanthology.org/2021.fever-1.10) (Funkquist, FEVER 2021)
ACL