As for knowledge-based question answering, a fundamental problem is to relax the assumption of answerable questions from simple questions to compound questions. Traditional approaches firstly detect topic entity mentioned in questions, then traverse the knowledge graph to find relations as a multi-hop path to answers, while we propose a novel approach to leverage simple-question answerers to answer compound questions. Our model consists of two parts: (i) a novel learning-to-decompose agent that learns a policy to decompose a compound question into simple questions and (ii) three independent simple-question answerers that classify the corresponding relations for each simple question. Experiments demonstrate that our model learns complex rules of compositionality as stochastic policy, which benefits simple neural networks to achieve state-of-the-art results on WebQuestions and MetaQA. We analyze the interpretable decomposition process as well as generated partitions. Knowledge-Based Question Answering (KBQA) is one of the most interesting approaches of answering a question, which bridges a curated knowledge base of tremendous facts to answerable questions. With question answering as a user-friendly interface, users can easily query a knowledge base through natural language, i.e., in their own words. In the past few years, many systems BID5 BID2 Yih et al., 2015; BID11 BID13 have achieved remarkable improvements in various datasets, such as WebQuestions BID5 , SimpleQuestions BID6 and MetaQA .However , most of them BID31 BID6 BID10 BID34 BID36 assume that only simple questions are answerable. Simple questions are questions that have only one relation from the topic entity to unknown tail entities (answers, usually substituted by an interrogative word) while compound questions are questions that have multiple 1 relations. For example , "Who are the daughters of Barack Obama?" is a simple question and "Who is the mother of the daughters of Barack Obama?" is a compound question which can be decomposed into two simple questions.In this paper, we aim to relax the assumption of answerable questions from simple questions to compound questions. Figure 1 illustrates the process of answering compound questions. Intuitively, to answer a compound question, traditional approaches firstly detect topic entity mentioned in the question, as the starting point for traversing the knowledge graph, then find a chain of multiple (≤ 3) relations as a multi-hop 2 path to golden answers.We propose a learning-to-decompose agent which assists simple-question answerers to solve compound questions directly. Our agent learns a policy for decomposing compound question into simple ones in a meaningful way, guided by the feedback from the downstream simple-question answerers.The goal of the agent is to produce partitions and compute the compositional structure of questions 1 We assume that the number of corresponding relations is at most three. 2 We are aware of the term multi-hop question in the literature. We argue that compound question is a better fit for the context of KBQA since multi-hop characterizes a path, not a question. As for document-based QA, multi-hop also refers to routing over multiple evidence to answers.Figure 1: An example of answering compound questions. Given a question Q, we first identify the topic entity e with entity linking. By relation detection, a movie-to-actor relation f 1 , an actor-tomovie relation f 2 and a movie-to-writer relation f 3 forms a path to the answers W i . Note that each relation f i corresponds to a part of the question. If we decomposes the question in a different way, we may find a movie-to-movie relation g as a shortcut, and g(e) = f 2 (f 1 (e)) = (f 2 • f 1 )(e) holds. Our model discovered such composite rules. See section 4 for further discussion.with maximum information utilization. The intuition is that encouraging the model to learn structural compositions of compound questions will bias the model toward better generalizations about how the meaning of a question is encoded in terms of compositional structures on sequences of words, leading to better performance on downstream question answering tasks.We demonstrate that our agent captures the semantics of compound questions and generate interpretable decomposition. Experimental results show that our novel approach achieves state-of-the-art performance in two challenging datasets (WebQuestions and MetaQA), without re-designing complex neural networks to answer compound questions. Understanding compound questions, in terms of The Principle of Semantic Compositionality BID20 , require one to decompose the meaning of a whole into the meaning of parts. While previous works focus on leveraging knowledge graph for generating a feasible path to answers, we Figure 4 : A continuous example of figure 1. The hollow circle indicates the corresponding action the agent takes for each time step. The upper half is the actual prediction while the lower half is a potential partition. Since we do not allow a word to join two partitions, the agent learns to separate "share" and "actors" into different partitions to maximize information utilization.propose a novel approach making full use of question semantics efficiently, in terms of the Principle of Semantic Compositionality.In other words, it is counterintuitive that compressing the whole meaning of a variable-length sentence to a fixed-length vector, which leaves the burden to the downstream relation classifier. In contrast, we assume that a compound question can be decomposed into three simple questions at most. Our model generates partitions by a learned policy given a question. The vector representations of each partition are then fed into the downstream relation classifier.While previous works focus on leveraging knowledge graph for generating a feasible path to answers, we propose a novel approach making full use of question semantics efficiently, in terms of the Principle of Semantic Compositionality.Our learning-to-decompose agent can also serve as a plug-and-play module for other question answering task that requires to understand compound questions. This paper is an example of how to help the simple-question answerers to understand compound questions. The answerable question assumption must be relaxed in order to generalize question answering.
We propose a learning-to-decompose agent that helps simple-question answerers to answer compound question over knowledge graph.
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