@inproceedings{mori-etal-2022-neural,
title = "Neural Machine Translation for Fact-checking Temporal Claims",
author = "Mori, Marco and
Papotti, Paolo and
Bellomarini, Luigi and
Giudice, Oliver",
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 Fifth Fact Extraction and VERification Workshop (FEVER)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.fever-1.8",
doi = "10.18653/v1/2022.fever-1.8",
pages = "78--82",
abstract = "Computational fact-checking aims at supporting the verification process of textual claims by exploiting trustworthy sources. However, there are large classes of complex claims that cannot be automatically verified, for instance those related to temporal reasoning. To this aim, in this work, we focus on the verification of economic claims against time series sources. Starting from given textual claims in natural language, we propose a neural machine translation approach to produce respective queries expressed in a recently proposed temporal fragment of the Datalog language. The adopted deep neural approach shows promising preliminary results for the translation of 10 categories of claims extracted from real use cases.",
}
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<abstract>Computational fact-checking aims at supporting the verification process of textual claims by exploiting trustworthy sources. However, there are large classes of complex claims that cannot be automatically verified, for instance those related to temporal reasoning. To this aim, in this work, we focus on the verification of economic claims against time series sources. Starting from given textual claims in natural language, we propose a neural machine translation approach to produce respective queries expressed in a recently proposed temporal fragment of the Datalog language. The adopted deep neural approach shows promising preliminary results for the translation of 10 categories of claims extracted from real use cases.</abstract>
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%0 Conference Proceedings
%T Neural Machine Translation for Fact-checking Temporal Claims
%A Mori, Marco
%A Papotti, Paolo
%A Bellomarini, Luigi
%A Giudice, Oliver
%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 Fifth Fact Extraction and VERification Workshop (FEVER)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F mori-etal-2022-neural
%X Computational fact-checking aims at supporting the verification process of textual claims by exploiting trustworthy sources. However, there are large classes of complex claims that cannot be automatically verified, for instance those related to temporal reasoning. To this aim, in this work, we focus on the verification of economic claims against time series sources. Starting from given textual claims in natural language, we propose a neural machine translation approach to produce respective queries expressed in a recently proposed temporal fragment of the Datalog language. The adopted deep neural approach shows promising preliminary results for the translation of 10 categories of claims extracted from real use cases.
%R 10.18653/v1/2022.fever-1.8
%U https://aclanthology.org/2022.fever-1.8
%U https://doi.org/10.18653/v1/2022.fever-1.8
%P 78-82
Markdown (Informal)
[Neural Machine Translation for Fact-checking Temporal Claims](https://aclanthology.org/2022.fever-1.8) (Mori et al., FEVER 2022)
ACL