stephaniebrandl/climate_fever_rationales

The Climate-Fever dataset was first collected and published by Diggelmann et al, 2020.
For our study, we are interested in token-level rationales which are not available from the initial publication of Climate-Fever. Therefore, we manually selected a subset of 102 claims (510 claim-evidence pairs) based on clarity of the claim formulation and balanced claim labels. Each sample was annotated on token-level by 3 annotators as either supporting the claim (label=1), contradicting the claim (label=-1) or neutral (label=0). Annotations were then averaged (rationale_numeric) and majority-voted (rationale_binary).

#column "label"
claim_label_dict = {
    '🙌 SUPPORT': 1, 
    '👎 REFUTE/CONTRADICT': -1, 
    '🤷 NOT ENOUGH INFO': 0, 
    '🤨 DISPUTE (SUPPORT and REFUTE)': 2}

#column "evidence_labels"
evidence_labels_dict = {
    0: "supports", 
    1: "refutes",
    2: "not enough info"}

More details can be found in
Stephanie Brandl* & Oliver Eberle* (2026) A Systematic Comparison between Extractive Self-Explanations and Human Rationales in Text Classification. Proceedings of the 6th Workshop on Trustworthy Natural Language Processing. (to appear)