  Our goal, in the context of open-domain textual question-answering (QA), is
to explain answers by showing the line of reasoning from what is known to the
answer, rather than simply showing a fragment of textual evidence (a
"rationale'"). If this could be done, new opportunities for understanding and
debugging the system's reasoning become possible. Our approach is to generate
explanations in the form of entailment trees, namely a tree of multipremise
entailment steps from facts that are known, through intermediate conclusions,
to the hypothesis of interest (namely the question + answer). To train a model
with this skill, we created ENTAILMENTBANK, the first dataset to contain
multistep entailment trees. Given a hypothesis (question + answer), we define
three increasingly difficult explanation tasks: generate a valid entailment
tree given (a) all relevant sentences (b) all relevant and some irrelevant
sentences, or (c) a corpus. We show that a strong language model can partially
solve these tasks, in particular when the relevant sentences are included in
the input (e.g., 35% of trees for (a) are perfect), and with indications of
generalization to other domains. This work is significant as it provides a new
type of dataset (multistep entailments) and baselines, offering a new avenue
for the community to generate richer, more systematic explanations.
