  We present SpaceQA, to the best of our knowledge the first open-domain QA
system in Space mission design. SpaceQA is part of an initiative by the
European Space Agency (ESA) to facilitate the access, sharing and reuse of
information about Space mission design within the agency and with the public.
We adopt a state-of-the-art architecture consisting of a dense retriever and a
neural reader and opt for an approach based on transfer learning rather than
fine-tuning due to the lack of domain-specific annotated data. Our evaluation
on a test set produced by ESA is largely consistent with the results originally
reported by the evaluated retrievers and confirms the need of fine tuning for
reading comprehension. As of writing this paper, ESA is piloting SpaceQA
internally.
