Conference PaperAugmenting Neural Networks with First-order LogicLi, Tao; Srikumar, VivekToday, 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 frame-work 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.2019-07-0110175282Proceedings of the 57th Annual Meeting of the Association for Computational Linguisticshttps://doi.org/10.18653/v1/P19-10281801446National Science Foundation