Recent advances in neural symbolic learning, such as DeepProbLog, extend probabilistic logic programs with neural predicates. Like graphical models, these probabilistic logic programs define a probability distribution over possible worlds, for which inference is computationally hard. We propose DeepStochLog, an alternative neural symbolic framework based on stochastic definite clause grammars, a type of stochastic logic program, which defines a probability distribution over possible derivations. More specifically, we introduce neural grammar rules into stochastic definite clause grammars to create a framework that can be trained end-to-end. We show that inference and learning in neural stochastic logic programming scale much better than for neural probabilistic logic programs. Furthermore, the experimental evaluation shows that DeepStochLog achieves state-of-the-art results on challenging neural symbolic learning tasks.

介绍了一种名为DeepStochLog的新型神经符号学习框架，其利用了一类名为随机确定性子句文法的概率式逻辑程序，其中引入了神经语法规则，可以实现端对端的训练，以实现比神经概率逻辑程序更高效的推理和学习。实验结果表明，DeepStochLog取得了具有挑战性的神经符号学习任务的最新成果。

深度随机逻辑编程：DeepStochLog