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 every possible interpretation of the question, and propose a set of disambiguated question-answer pairs. In this paper, we present a model that aggregates and combines evidence from multiple passages to generate question-answer pairs . Particularly, our model reads a large number of passages to find as many interpretations as possible. In addition, we propose a novel round-trip prediction approach to  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. On the recently introduced AmbigQA open-domain question answering dataset, our model, named Refuel, achieves a new state-of-the-art , outperforming the previous best model by a large margin. We also conduct comprehensive analyses to validate the effectiveness of our proposed round-trip prediction .
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 URL
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every possible interpretation
possible interpretations
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propose a set of disambiguated question-answer pairs.
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.
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generate
adaptively predict a single answer or a set of
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. Particularly, our model reads a large number of passages to find as many interpretations as possible.
for ambiguous questions.
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iteratively
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On the recently introduced AmbigQA open-domain question answering dataset, our
Our
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, outperforming the previous best model by a large margin. We also conduct comprehensive analyses to validate the effectiveness of our
performance on the AmbigQA dataset, and shows competitive performance on NQ-Open and TriviaQA. The
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.
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 URL