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

Knowledgeable Reader: Enhancing Cloze-Style Reading Comprehension with External Commonsense Knowledge