Directly reading documents and being able to answer questions from them is an
unsolved challenge. To avoid its inherent difficulty, question answering (QA)
has been directed towards using Knowledge Bases (KBs) instead, which has proven
effective. Unfortunately KBs often suffer from being too restrictive, as the
schema cannot support certain types of answers, and too sparse, e.g. Wikipedia
contains much more information than Freebase. In this work we introduce a new
method, Key-Value Memory Networks, that makes reading documents more viable by
utilizing different encodings in the addressing and output stages of the memory
read operation. To compare using KBs, information extraction or Wikipedia
documents directly in a single framework we construct an analysis tool,
WikiMovies, a QA dataset that contains raw text alongside a preprocessed KB, in
the domain of movies. Our method reduces the gap between all three settings. It
also achieves state-of-the-art results on the existing WikiQA benchmark.

该论文介绍了一种新方法，利用 Key-Value Memory Networks 技术，使得从文本中读取信息并回答问题成为可能，并在电影领域的 QA 数据集 WikiMovies 上进行了实验和检测，结果表明该方法对比使用 KB 或信息提取或 Wikipedia 直接处理的方法在表现上更优，表现出 state-of-the-art 的效果。