Top k Memory Candidates in Memory Networks for Common Sense Reasoning
  Successful completion of reasoning task requires the agent to have relevant
prior knowledge or some given context of the world dynamics. Usually, the
information provided to the system for a reasoning task is just the query or
some supporting story, which is often not enough for common reasoning tasks.
The goal here is that, if the information provided along the question is not
sufficient to correctly answer the question, the model should choose k most
relevant documents that can aid its inference process. In this work, the model
dynamically selects top k most relevant memory candidates that can be used to
successfully solve reasoning tasks. Experiments were conducted on a subset of
Winograd Schema Challenge (WSC) problems to show that the proposed model has
the potential for commonsense reasoning. The WSC is a test of machine
intelligence, designed to be an improvement on the Turing test.
