This dataset contains 98k 2-hop explanations for questions in the QASC dataset, with annotations indicating if they are valid (~25k) or invalid (~73k) explanations.
This repository addresses the current lack of training data for distinguish valid multihop explanations from invalid, by providing three new datasets. The main one, eQASC, contains 98k explanation annotations for the multihop question answering dataset QASC, and is the first that annotates multiple candidate explanations for each answer.
The second dataset, eQASC-perturbed, is constructed by crowd-sourcing perturbations (while preserving their validity) of a subset of explanations in QASC, to test consistency and generalization of explanation prediction models. The third dataset eOBQA is constructed by adding explanation annotations to the OBQA dataset to test generalization of models trained on eQASC.Source: Allen Institute for AI
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