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In the open book question answering (OBQA) task, selecting the relevantpassages and sentences from distracting information is crucial to reason theanswer to a question. HotpotQA dataset is designed to teach and evaluatesystems to do both passage ranking and sentence selection. Many existingframeworks use separate models to select relevant passages and sentencesrespectively. Such systems not only have high complexity in terms of theparameters of models but also fail to take the advantage of training these twotasks together since one task can be beneficial for the other one. In thiswork, we present a simple yet effective framework to address these limitationsby jointly ranking passages and selecting sentences. Furthermore, we proposeconsistency and similarity constraints to promote the correlation andinteraction between passage ranking and sentence selection.The experimentsdemonstrate that our framework can achieve competitive results with previoussystems and outperform the baseline by 28\% in terms of exact matching ofrelevant sentences on the HotpotQA dataset.

A Simple Approach to Jointly Rank Passages and Select Relevant Sentences in the OBQA Context