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Learning to activate logic rules for textual reasoning
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DOI：10.1016/j.neunet.2018.06.012.png)
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Abstract
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Most current textual reasoning models cannot learn human-like reasoning process, and thus lack interpretability and logical accuracy. To help address this issue, we propose a novel reasoning model which learns to activate logic rules explicitly via deep reinforcement learning. It takes the form of Memory Networks but features a special memory that stores relational tuples, mimicking the Image Schema'' in human cognitive activities. We redefine textual reasoning as a sequential decision-making process modifying or retrieving from the memory, where logic rules serve as state-transition functions. Activating logic rules for reasoning involves two problems: variable binding and relation activating, and this is a first step to solve them jointly. Our model achieves an average error rate of 0.7% on bAbI-20, a widely-used synthetic reasoning benchmark, using less than 1k training samples and no supporting facts. (C) 2018 Elsevier Ltd. All rights reserved.
Keywords:
Natural language reasoning
Memory networks
Image schema
Logic rules
Reinforcement learning
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