%0 Journal Article %1 weston2014memory %A Weston, Jason %A Chopra, Sumit %A Bordes, Antoine %D 2014 %K deep-learning memory %T Memory Networks %U http://arxiv.org/abs/1410.3916 %X 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.

@article{weston2014memory, abstract = {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.}, added-at = {2019-03-21T13:37:52.000+0100}, author = {Weston, Jason and Chopra, Sumit and Bordes, Antoine}, biburl = {https://www.bibsonomy.org/bibtex/2f3d51ba55acd2f7b50c18830c1f1fbe6/kirk86}, description = {[1410.3916] Memory Networks}, interhash = {accbef7c6a665a063f7d6dc8f187de88}, intrahash = {f3d51ba55acd2f7b50c18830c1f1fbe6}, keywords = {deep-learning memory}, note = {cite arxiv:1410.3916}, timestamp = {2019-03-21T13:37:52.000+0100}, title = {Memory Networks}, url = {http://arxiv.org/abs/1410.3916}, year = 2014 }