  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.
