2015

Towards Neural Network-based Reasoning

Peng, Baolin, Lu, Zhengdong, Li, Hang et al.

Understand

We propose Neural Reasoner, a framework for neural network-based reasoning over natural language sentences.

Given a question, Neural Reasoner can infer over multiple supporting facts and find an answer to the question in specific forms.
Neural Reasoner has 1) a specific interaction-pooling mechanism, allowing it to examine multiple facts, and 2) a deep architecture, allowing it to model the complicated logical relations in reasoning tasks.
Assuming no particular structure exists in the question and facts, Neural Reasoner is able to accommodate different types of reasoning and different forms of language expressions.

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