Separating Answers from Queries for Neural Reading Comprehension

Understand

We present a novel neural architecture for answering queries, designed to optimally leverage explicit support in the form of query-answer memories.

• Our model is able to refine and update a given query while separately accumulating evidence for predicting the answer.
• Its architecture reflects this separation with dedicated embedding matrices and loosely connected information pathways (modules) for updating the query and accumulating evidence.
• This separation of responsibilities effectively decouples the search for query related support and the prediction of the answer.