Papers by Tom Calamai

2 papers
MAFALDA: A Benchmark and Comprehensive Study of Fallacy Detection and Classification (2024.naacl-long)

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Challenge: Fallacy classification is a task of broad importance due to advances in deep learning and availability of more data.
Approach: They propose a new annotation scheme tailored for subjective NLP tasks and a method designed to handle subjectivity.
Outcome: The proposed approach integrates existing fallacy classification datasets with new ones.
Benchmarking the Benchmarks: Reproducing Climate-Related NLP Tasks (2025.findings-acl)

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Challenge: a recent study examines the use of climate-related natural language processing (NLP) for climate-relevant tasks.
Approach: They perform a reproducibility study on 8 tasks and 29 datasets, testing 6 models.
Outcome: The proposed models are based on 8 tasks and 29 datasets.

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