  We describe a new class of learning models called memory networks. Memory
networks reason with inference components combined with a long-term memory
component; they learn how to use these jointly. The long-term memory can be
read and written to, with the goal of using it for prediction. We investigate
these models in the context of question answering (QA) where the long-term
memory effectively acts as a (dynamic) knowledge base, and the output is a
textual response. We evaluate them on a large-scale QA task, and a smaller, but
more complex, toy task generated from a simulated world. In the latter, we show
the reasoning power of such models by chaining multiple supporting sentences to
answer questions that require understanding the intension of verbs.
