Megh Thakkar, Quentin Fournier, et al.
ACL 2024
In this talk, I will discuss our recent work on applying and investigating language model (LM)-based argument mining technologies in real-world scenarios, including fact-checking misinformation that misrepresents scientific publications and tackling traditional argument mining tasks in various out-of-distribution (OOD) scenarios. First, I will discuss our work on reconstructing and grounding fallacies in misrepresented science, in which health-related misinformation claims often falsely cite a credible biomedical publication as evidence. I will present a new argumentation theoretical model for fallacious reasoning, together with a new dataset for real-world misinformation detection that misrepresents biomedical publications. In the second part of the talk, I will discuss our findings on LMs' capabilities for three OOD scenarios (topic shift, domain shift, and language shift) across eleven argument mining tasks.
Megh Thakkar, Quentin Fournier, et al.
ACL 2024
Shivashankar Subramanian, Ioana Baldini, et al.
IAAI 2020
Hammad Ayyubi, Rahul Lokesh, et al.
ACL 2023
Tahira Naseem, GX Xu, et al.
ACL 2024