Memory networks

Jason Weston, Sumit Chopra, Antoine Bordes

Research output: Contribution to conferencePaperpeer-review

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.

Original languageEnglish (US)
StatePublished - 2015
Event3rd International Conference on Learning Representations, ICLR 2015 - San Diego, United States
Duration: May 7 2015May 9 2015

Conference

Conference3rd International Conference on Learning Representations, ICLR 2015
Country/TerritoryUnited States
CitySan Diego
Period5/7/155/9/15

ASJC Scopus subject areas

  • Education
  • Computer Science Applications
  • Linguistics and Language
  • Language and Linguistics

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