Differentiable Logic Programming for Distant Supervision
This addresses the challenge of efficient and accurate learning in Neural-Symbolic AI for applications with distant supervision, representing an incremental improvement over prior methods.
The paper tackles the problem of learning with distant supervision where direct labels are unavailable by integrating neural networks with logic programming in a differentiable way, resulting in matching or exceeding accuracy and speeding up learning compared to existing methods.
We introduce a new method for integrating neural networks with logic programming in Neural-Symbolic AI (NeSy), aimed at learning with distant supervision, in which direct labels are unavailable. Unlike prior methods, our approach does not depend on symbolic solvers for reasoning about missing labels. Instead, it evaluates logical implications and constraints in a differentiable manner by embedding both neural network outputs and logic programs into matrices. This method facilitates more efficient learning under distant supervision. We evaluated our approach against existing methods while maintaining a constant volume of training data. The findings indicate that our method not only matches or exceeds the accuracy of other methods across various tasks but also speeds up the learning process. These results highlight the potential of our approach to enhance both accuracy and learning efficiency in NeSy applications.