Parallel Neurosymbolic Integration with Concordia

An alternative to stratified is parallel integration. In contrast to stratified frameworks, parallel integration applies in settings Parallel neurosymbolic architectures have been in which the same task can be solved both symbolically applied effectively in NLP by distilling knowledge and sub-symbolically and the aim is to increase the accuracy from a logic theory into a deep model. However, of the end task by distilling knowledge from the logic prior art faces several limitations including component into the neural one and vice versa. Two parallel supporting restricted forms of logic theories and neurosymbolic frameworks have been proposed recently: relying on the assumption of independence between Teacher-Student (T-S) by Hu et al. (Hu et al., 2016a;b) and the logic and the deep network.