Key-Value Memory Networks for Directly Reading Documents

Directly reading documents and being able to answer questions from them is a
key problem. To avoid its inherent difficulty, question answering (QA) has been
directed towards using Knowledge Bases (KBs) instead, which has proven
effective. Unfortunately KBs suffer from often 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,
MovieQA, a QA dataset in the domain of movies. Our method closes the gap
between all three settings. It also achieves state-of-the-art results on the
existing WikiQA benchmark.