Top k Memory Candidates in Memory Networks for Common Sense Reasoning

TL;DR

This paper proposes a model that dynamically selects the top k relevant memory candidates to improve commonsense reasoning in tasks like the Winograd Schema Challenge, enhancing the inference process with relevant prior knowledge.

Contribution

It introduces a method for selecting top k relevant memory candidates dynamically to aid reasoning, addressing the insufficiency of provided information.

Findings

The model effectively identifies relevant memory candidates for reasoning.

Improved performance on Winograd Schema Challenge tasks.

Demonstrates potential for enhanced commonsense reasoning.

Abstract

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.