When answering natural language questions over knowledge bases (KBs),
different question components and KB aspects play different roles. However,
most existing embedding-based methods for knowledge base question answering
(KBQA) ignore the subtle inter-relationships between the question and the KB
(e.g., entity types, relation paths and context). In this work, we propose to
directly model the two-way flow of interactions between the questions and the
KB via a novel Bidirectional Attentive Memory Network, called BAMnet. Requiring
no external resources and only very few hand-crafted features, on the
WebQuestions benchmark, our method significantly outperforms existing
information-retrieval based methods, and remains competitive with
(hand-crafted) semantic parsing based methods. Also, since we use attention
mechanisms, our method offers better interpretability compared to other
baselines.

本文提出了一种新型的双向注意力记忆网络（BAMnet），通过直接建模问题和知识库之间的交互作用来回答自然语言问题，并使用注意机制使解释性比其他基准线更好，在 WebQuestions 基准测试中，我们的方法明显优于现有的基于信息检索的方法，并且在（手工制作的）语义解析方法基础上依然具有竞争力。