Learning to Order Sub-questions for Complex Question Answering

Answering complex questions involving multiple entities and relations is a challenging task. Logically, the answer to a complex question should be derived by decomposing the complex question into multiple simple sub-questions and then answering those sub-questions. Existing work has followed this strategy but has not attempted to optimize the order how those sub-questions are answered. As a result, the sub-questions are answered in an arbitrary order, leading to larger search space and higher risk of missing an answer. In this paper, we propose a novel reinforcement learning (RL) approach to answering complex questions that can learn a policy to dynamically decide which sub-question should be answered at each state of reasoning. We leverage the expected value-variance criterion to enable the learned policy to balance between the risk and utility of answering a sub-question. Experiment results show that the RL approach can substantially improve the optimal-ity of ordering the sub-questions, leading to improved accuracy of question answering. The proposed method for learning to order sub-questions is general and can thus be potentially combined with many existing ideas for answering complex questions to enhance their performance. Introduction Real-world questions can be complex, involving multiple interrelated entities and relations, which we refer to as complex questions . For example, "who writes Harry Potter" is a simple question that only involves a single entity and a relation, while "Which city is the filming location of the book written by J.K.Rowling and held Olympics?" is a complex question, which consists of multiple entities and relations. How to automatically answer such complex questions is a significant scientific challenge because it requires a system to capture the dependencies between different components of the questions and reason over them. Recently, some recent work has attempted to tackle such complex questions (Talmor and Berant 2018; Iyyer, Yih, and Chang 2016; Min et al. 2019; Zhang et al. 2019), usually by decomposing a complex question into a sequence of simple questions and answering them based on a computation tree derived from the original question that can capture the dependency between sub-questions as shown in Figure 1.