Despite the rapid progress in multihop question-answering (QA), models still
have trouble explaining why an answer is correct, with limited explanation
training data available to learn from. To address this, we introduce three
explanation datasets in which explanations formed from corpus facts are
annotated. Our first dataset, eQASC, contains over 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. We show that this
data can be used to significantly improve explanation quality (+14% absolute F1
over a strong retrieval baseline) using a BERT-based classifier, but still
behind the upper bound, offering a new challenge for future research. We also
explore a delexicalized chain representation in which repeated noun phrases are
replaced by variables, thus turning them into generalized reasoning chains (for
example: "X is a Y" AND "Y has Z" IMPLIES "X has Z"). We find that generalized
chains maintain performance while also being more robust to certain
perturbations.

本研究引入了三种不同类型的解释数据集，发现基于 BERT 分类器的方法能够显著提高解释质量，同时通过使用广义推理链，使得对某些扰动更具鲁棒性。