  Scenario-based question answering (SQA) requires retrieving and reading
paragraphs from a large corpus to answer a question which is contextualized by
a long scenario description. Since a scenario contains both keyphrases for
retrieval and much noise, retrieval for SQA is extremely difficult. Moreover,
it can hardly be supervised due to the lack of relevance labels of paragraphs
for SQA. To meet the challenge, in this paper we propose a joint
retriever-reader model called JEEVES where the retriever is implicitly
supervised only using QA labels via a novel word weighting mechanism. JEEVES
significantly outperforms a variety of strong baselines on multiple-choice
questions in three SQA datasets.
