  Building compositional explanations requires models to combine two or more
facts that, together, describe why the answer to a question is correct.
Typically, these "multi-hop" explanations are evaluated relative to one (or a
small number of) gold explanations. In this work, we show these evaluations
substantially underestimate model performance, both in terms of the relevance
of included facts, as well as the completeness of model-generated explanations,
because models regularly discover and produce valid explanations that are
different than gold explanations. To address this, we construct a large corpus
of 126k domain-expert (science teacher) relevance ratings that augment a corpus
of explanations to standardized science exam questions, discovering 80k
additional relevant facts not rated as gold. We build three strong models based
on different methodologies (generation, ranking, and schemas), and empirically
show that while expert-augmented ratings provide better estimates of
explanation quality, both original (gold) and expert-augmented automatic
evaluations still substantially underestimate performance by up to 36% when
compared with full manual expert judgements, with different models being
disproportionately affected. This poses a significant methodological challenge
to accurately evaluating explanations produced by compositional reasoning
models.
