  In open-domain question answering, questions are highly likely to be
ambiguous because users may not know the scope of relevant topics when
formulating them. Therefore, a system needs to find possible interpretations of
the question, and predict one or multiple plausible answers. When multiple
plausible answers are found, the system should rewrite the question for each
answer to resolve the ambiguity. In this paper, we present a model that
aggregates and combines evidence from multiple passages to adaptively predict a
single answer or a set of question-answer pairs for ambiguous questions. In
addition, we propose a novel round-trip prediction approach to iteratively
generate additional interpretations that our model fails to find in the first
pass, and then verify and filter out the incorrect question-answer pairs to
arrive at the final disambiguated output. Our model, named Refuel, achieves a
new state-of-the-art performance on the AmbigQA dataset, and shows competitive
performance on NQ-Open and TriviaQA. The proposed round-trip prediction is a
model-agnostic general approach for answering ambiguous open-domain questions,
which improves our Refuel as well as several baseline models. We release source
code for our models and experiments at
https://github.com/amzn/refuel-open-domain-qa.
