@inproceedings{li-srikumar-2019-augmenting,
title = "Augmenting Neural Networks with First-order Logic",
author = "Li, Tao and
Srikumar, Vivek",
editor = "Korhonen, Anna and
Traum, David and
M{\`a}rquez, Llu{\'\i}s",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1028",
doi = "10.18653/v1/P19-1028",
pages = "292--302",
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: 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.",
}
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%0 Conference Proceedings
%T Augmenting Neural Networks with First-order Logic
%A Li, Tao
%A Srikumar, Vivek
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F li-srikumar-2019-augmenting
%X 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.
%R 10.18653/v1/P19-1028
%U https://aclanthology.org/P19-1028
%U https://doi.org/10.18653/v1/P19-1028
%P 292-302
Markdown (Informal)
[Augmenting Neural Networks with First-order Logic](https://aclanthology.org/P19-1028) (Li & Srikumar, ACL 2019)
ACL
- Tao Li and Vivek Srikumar. 2019. Augmenting Neural Networks with First-order Logic. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 292–302, Florence, Italy. Association for Computational Linguistics.