A Context-Aware Approach for Detecting Worth-Checking Claims in Political Debates
Pepa Gencheva, Preslav Nakov, Lluís Màrquez, Alberto Barrón-Cedeño, Ivan Koychev
Abstract
In the context of investigative journalism, we address the problem of automatically identifying which claims in a given document are most worthy and should be prioritized for fact-checking. Despite its importance, this is a relatively understudied problem. Thus, we create a new corpus of political debates, containing statements that have been fact-checked by nine reputable sources, and we train machine learning models to predict which claims should be prioritized for fact-checking, i.e., we model the problem as a ranking task. Unlike previous work, which has looked primarily at sentences in isolation, in this paper we focus on a rich input representation modeling the context: relationship between the target statement and the larger context of the debate, interaction between the opponents, and reaction by the moderator and by the public. Our experiments show state-of-the-art results, outperforming a strong rivaling system by a margin, while also confirming the importance of the contextual information.- Anthology ID:
- R17-1037
- Volume:
- Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017
- Month:
- September
- Year:
- 2017
- Address:
- Varna, Bulgaria
- Venue:
- RANLP
- SIG:
- Publisher:
- INCOMA Ltd.
- Note:
- Pages:
- 267–276
- Language:
- URL:
- https://doi.org/10.26615/978-954-452-049-6_037
- DOI:
- 10.26615/978-954-452-049-6_037
- Cite (ACL):
- Pepa Gencheva, Preslav Nakov, Lluís Màrquez, Alberto Barrón-Cedeño, and Ivan Koychev. 2017. A Context-Aware Approach for Detecting Worth-Checking Claims in Political Debates. In Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017, pages 267–276, Varna, Bulgaria. INCOMA Ltd..
- Cite (Informal):
- A Context-Aware Approach for Detecting Worth-Checking Claims in Political Debates (Gencheva et al., RANLP 2017)
- PDF:
- https://doi.org/10.26615/978-954-452-049-6_037