Augmenting Neural Networks with First-order Logic
Tao Li, Vivek Srikumar · 2019
Today, the dominant paradigm for training neural networks involves minimizing task loss on a large dataset.Using world knowledge to inform a model, and yet retain the ability to perform end-to-end training remains an open question.In this paper, we present a novel framework for introducing declarative knowledge to neural network architectures in order to guide training and prediction.Our framework systematically compiles logical statements into computation graphs that augment a neural network without extra learnable parameters or manual redesign.We evaluate our modeling strategy on three tasks: machine comprehension, natural language inference, and text chunking.Our experiments show that knowledge-augmented networks can strongly improve over baselines, especially in low-data regimes.Gaius Julius Caesar (July 100 BC -15 March 44 BC), Roman general, statesman, Consul and notable author of Latin prose, played a critical role in the events that led to the demise of the Roman Republic and the rise of the Roman Empire through his various military campaigns.