Abstract
Artificial intelligence has come a long way, but how close are we actually to creating a machine that “thinks” like a human, acts like a human? Common sense reasoning is one of the large obstacles that prevent this from happening: it is difficult for the computer to establish similar associations that the human mind intuitively and implicitly connects. Association learning establishes a model that simulates the acquisition of association from examples, which is often believed to be one of the fundamental abilities for humans to possess common sense. However, “labelled” association data is still scarce, and it usually has to be labelled manually, making it very costly. Our research aims are to look into the active learning in the common sense association reasoning that poses issues on how to limit data needed to train an association network model effectively. We propose a dual network with positive and negative associations, based on a bipartite NMF association model. In this model, we extend the previously all-positive network model to include a negative network, which makes it possible to distinguish between an unknown association pair and a pair that is verified to have no association. This is crucial to an effective active learning, since it allows to extract more data out of the answers provided by consulting an oracle. We show that the extended model creates 2% improvement over the previous bipartite model before applying active learning, using the same evaluation methods as the previous model. This improvement can be further bolstered using active learning. We tested 4 selection strategies for active learning, including random (as baseline), conflict, uncertainty, and hybrid, and found that either of the three proposed active learning strategies scored a better precision increase than the baseline, with the hybrid one achieving up to twice the improvement in precision compared to random selection.