Year: 2016
Type: conference-paper
Abstract: Combining deep neural networks with structured logic rules is desirable to harness flexibility and reduce uninterpretability of the neural models.We propose a general framework capable of enhancing various types of neural networks (e.g., CNNs and RNNs) with declarative first-order logic rules.Specifically, we develop an iterative distillation method that transfers the structured information of logic rules into the weights of neural networks.We deploy the framework on a CNN for sentiment analysis, and an RNN for named entity recognition.With a few highly intuitive rules, we obtain substantial improvements and achieve state-of-the-art or comparable results to previous best-performing systems.
DOI: 10.18653/v1/p16-1228
Institution: Carnegie Mellon University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Funder: National Science Foundation
Awards: III: Small: Collaborative Research: Efficient, Nonparametric and Local-Minimum-Free Latent Variable Models: With Application to Large-Scale Computer Vision and Genomics, BIGDATA: F: DKA: Collaborative Research: Theory and Algorithms for Parallel Probabilistic Inference with Big Data, via Big Model, in Realistic Distributed Computing Environments
OpenAlex ID: W2963687836