1 code implementation • 25 Oct 2022 • Max Glockner, Yufang Hou, Iryna Gurevych
In our analysis, we show that, by design, existing NLP task definitions for fact-checking cannot refute misinformation as professional fact-checkers do for the majority of claims.
2 code implementations • 1 Apr 2021 • Max Glockner, Ieva Staliūnaitė, James Thorne, Gisela Vallejo, Andreas Vlachos, Iryna Gurevych
Automated fact-checking systems verify claims against evidence to predict their veracity.
1 code implementation • EMNLP 2021 • Andreas Rücklé, Gregor Geigle, Max Glockner, Tilman Beck, Jonas Pfeiffer, Nils Reimers, Iryna Gurevych
Massively pre-trained transformer models are computationally expensive to fine-tune, slow for inference, and have large storage requirements.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Max Glockner, Ivan Habernal, Iryna Gurevych
We propose a differentiable training-framework to create models which output faithful rationales on a sentence level, by solely applying supervision on the target task.
2 code implementations • ACL 2018 • Max Glockner, Vered Shwartz, Yoav Goldberg
We create a new NLI test set that shows the deficiency of state-of-the-art models in inferences that require lexical and world knowledge.