  This paper proposes an iterative inference algorithm for multi-hop
explanation regeneration, that retrieves relevant factual evidence in the form
of text snippets, given a natural language question and its answer. Combining
multiple sources of evidence or facts for multi-hop reasoning becomes
increasingly hard when the number of sources needed to make an inference grows.
Our algorithm copes with this by decomposing the selection of facts from a
corpus autoregressively, conditioning the next iteration on previously selected
facts. This allows us to use a pairwise learning-to-rank loss. We validate our
method on datasets of the TextGraphs 2019 and 2020 Shared Tasks for explanation
regeneration. Existing work on this task either evaluates facts in isolation or
artificially limits the possible chains of facts, thus limiting multi-hop
inference. We demonstrate that our algorithm, when used with a pre-trained
transformer model, outperforms the previous state-of-the-art in terms of
precision, training time and inference efficiency.
