Neural Machine Translation for Fact-checking Temporal Claims
Marco Mori, Paolo Papotti, Luigi Bellomarini, Oliver Giudice
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.- Anthology ID:
- 2022.fever-1.8
- Volume:
- Proceedings of the Fifth Fact Extraction and VERification Workshop (FEVER)
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Editors:
- Rami Aly, Christos Christodoulopoulos, Oana Cocarascu, Zhijiang Guo, Arpit Mittal, Michael Schlichtkrull, James Thorne, Andreas Vlachos
- Venue:
- FEVER
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 78–82
- Language:
- URL:
- https://aclanthology.org/2022.fever-1.8
- DOI:
- 10.18653/v1/2022.fever-1.8
- Cite (ACL):
- Marco Mori, Paolo Papotti, Luigi Bellomarini, and Oliver Giudice. 2022. Neural Machine Translation for Fact-checking Temporal Claims. In Proceedings of the Fifth Fact Extraction and VERification Workshop (FEVER), pages 78–82, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- Neural Machine Translation for Fact-checking Temporal Claims (Mori et al., FEVER 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2022.fever-1.8.pdf