Asking Complex Questions with Multi-hop Answer-focused Reasoning
This addresses the limitation of existing methods that focus on simple single-hop questions, enabling more complex question generation for natural language processing applications.
The paper tackles the problem of generating complex multi-hop questions from documents, proposing a model that uses answer-focused reasoning on an entity graph to incorporate word-level and document-level semantics. It demonstrates effectiveness on the HOTPOTQA dataset, serving as a baseline for future work.
Asking questions from natural language text has attracted increasing attention recently, and several schemes have been proposed with promising results by asking the right question words and copy relevant words from the input to the question. However, most state-of-the-art methods focus on asking simple questions involving single-hop relations. In this paper, we propose a new task called multihop question generation that asks complex and semantically relevant questions by additionally discovering and modeling the multiple entities and their semantic relations given a collection of documents and the corresponding answer 1. To solve the problem, we propose multi-hop answer-focused reasoning on the grounded answer-centric entity graph to include different granularity levels of semantic information including the word-level and document-level semantics of the entities and their semantic relations. Through extensive experiments on the HOTPOTQA dataset, we demonstrate the superiority and effectiveness of our proposed model that serves as a baseline to motivate future work.