  In this paper, we propose a new deep learning approach, called neural
association model (NAM), for probabilistic reasoning in artificial
intelligence. We propose to use neural networks to model association between
any two events in a domain. Neural networks take one event as input and compute
a conditional probability of the other event to model how likely these two
events are to be associated. The actual meaning of the conditional
probabilities varies between applications and depends on how the models are
trained. In this work, as two case studies, we have investigated two NAM
structures, namely deep neural networks (DNN) and relation-modulated neural
nets (RMNN), on several probabilistic reasoning tasks in AI, including
recognizing textual entailment, triple classification in multi-relational
knowledge bases and commonsense reasoning. Experimental results on several
popular datasets derived from WordNet, FreeBase and ConceptNet have all
demonstrated that both DNNs and RMNNs perform equally well and they can
significantly outperform the conventional methods available for these reasoning
tasks. Moreover, compared with DNNs, RMNNs are superior in knowledge transfer,
where a pre-trained model can be quickly extended to an unseen relation after
observing only a few training samples. To further prove the effectiveness of
the proposed models, in this work, we have applied NAMs to solving challenging
Winograd Schema (WS) problems. Experiments conducted on a set of WS problems
prove that the proposed models have the potential for commonsense reasoning.
