  We introduce a neural reading comprehension model that integrates external
commonsense knowledge, encoded as a key-value memory, in a cloze-style setting.
Instead of relying only on document-to-question interaction or discrete
features as in prior work, our model attends to relevant external knowledge and
combines this knowledge with the context representation before inferring the
answer. This allows the model to attract and imply knowledge from an external
knowledge source that is not explicitly stated in the text, but that is
relevant for inferring the answer. Our model improves results over a very
strong baseline on a hard Common Nouns dataset, making it a strong competitor
of much more complex models. By including knowledge explicitly, our model can
also provide evidence about the background knowledge used in the RC process.
