Tao Li and Vivek Srikumar
ACL 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: ma- chine comprehension, natural language inference, and text chunking. Our experiments show that knowledge-augmented networks can strongly improve over baselines, especially in low-data regimes.


Bib Entry

      author    = {Li, Tao and Srikumar, Vivek},
      title     = {Augmenting Neural Networks with First-order Logic},
      booktitle = {Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics},
      year      = {2019}