Knowledge base question answering (KBQA) has attracted a lot of interest in recent years, especially for complex questions which require multiple facts to answer. Question decomposition is a promising way to answer complex questions. Existing decomposition methods split the question into sub-questions according to a single compositionality type, which is not sufficient for questions involving multiple compositionality types. In this paper, we propose Question Decomposition Tree (QDT) to represent the structure of complex questions. Inspired by recent advances in natural language generation (NLG), we present a two-staged method called Clue-Decipher to generate QDT. It can leverage the strong ability of NLG model and simultaneously preserve the original questions. To verify that QDT can enhance KBQA task, we design a decomposition-based KBQA system called QDTQA. Extensive experiments show that QDTQA outperforms previous state-of-the-art methods on ComplexWebQuestions dataset. Besides, our decomposition method improves an existing KBQA system by 12% and sets a new state-of-the-art on LC-QuAD 1.0.

翻译：知识库问答（KBQA）近年来引起了广泛关注，尤其是需要多个事实才能回答的复杂问题。问题分解是回答复杂问题的一种有效方法。现有的分解方法根据单一组合类型将问题拆分为子问题，这对于涉及多种组合类型的问题而言并不充分。本文提出问题分解树（QDT）来表示复杂问题的结构。受自然语言生成（NLG）领域最新进展的启发，我们提出了一种名为Clue-Decipher的两阶段方法来生成QDT。该方法既能利用NLG模型的强大能力，又能同时保留原始问题。为验证QDT能够增强KBQA任务，我们设计了一个基于分解的KBQA系统QDTQA。大量实验表明，QDTQA在ComplexWebQuestions数据集上优于先前的最优方法。此外，我们的分解方法将现有KBQA系统提升了12%，并在LC-QuAD 1.0数据集上创造了新的最优结果。