Augmenting Neural Networks with First-order Logic

Tao Li and Vivek Srikumar

Annual Meeting of the Association for Computational Linguistics (ACL), 2019.

Abstract

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

@inproceedings{li2019augmenting,
  author = {Li, Tao and Srikumar, Vivek},
  title = {{Augmenting Neural Networks with First-order Logic}},
  booktitle = {Annual Meeting of the Association for Computational Linguistics (ACL)},
  year = {2019}
}

Vivek Srikumar