  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. Despite the model complexity, Neural Reasoner can still be trained
effectively in an end-to-end manner. Our empirical studies show that Neural
Reasoner can outperform existing neural reasoning systems with remarkable
margins on two difficult artificial tasks (Positional Reasoning and Path
Finding) proposed in [8]. For example, it improves the accuracy on Path
Finding(10K) from 33.4% [6] to over 98%.

Artificial Intelligence;Computation and Language;Machine Learning;Neural and Evolutionary Computing