Explanations refer to additional information or context provided by the follow-up questions in interpretable multi-hop question answering. These questions are generated to seek clarification or further details, contributing to a more comprehensive understanding of the multi-hop reasoning process and facilitating the extraction of final answers.

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Generating Followup Questions for Interpretable Multi hop Question Answering

Generating Followup Questions for Interpretable Multi hop Question Answering We propose a framework for answering open domain multi hop questions in which partial information is read and used to generate followup questions, to finally be answered by a pretrained single hop answer extractor. This framework makes each hop interpretable, and makes the retrieval associated with later hops as flexible and specific as for the first hop. As a first instantiation of this framework, we train a pointer generator network to predict followup questions based on the question and partial information. This provides a novel application of a neural question generation network, which is applied to give weak ground truth single hop followup questions based on the final answers and their supporting facts. Learning to generate followup questions that select the relevant answer spans against downstream supporting facts, while avoiding distracting premises, poses an exciting semantic challenge for text generation. We present an evaluation using the two hop bridge questions of HotpotQA