@inproceedings{li-etal-2019-logic,
title = "A Logic-Driven Framework for Consistency of Neural Models",
author = "Li, Tao and
Gupta, Vivek and
Mehta, Maitrey and
Srikumar, Vivek",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1405",
doi = "10.18653/v1/D19-1405",
pages = "3924--3935",
abstract = "While neural models show remarkable accuracy on individual predictions, their internal beliefs can be inconsistent across examples. In this paper, we formalize such inconsistency as a generalization of prediction error. We propose a learning framework for constraining models using logic rules to regularize them away from inconsistency. Our framework can leverage both labeled and unlabeled examples and is directly compatible with off-the-shelf learning schemes without model redesign. We instantiate our framework on natural language inference, where experiments show that enforcing invariants stated in logic can help make the predictions of neural models both accurate and consistent.",
}
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<abstract>While neural models show remarkable accuracy on individual predictions, their internal beliefs can be inconsistent across examples. In this paper, we formalize such inconsistency as a generalization of prediction error. We propose a learning framework for constraining models using logic rules to regularize them away from inconsistency. Our framework can leverage both labeled and unlabeled examples and is directly compatible with off-the-shelf learning schemes without model redesign. We instantiate our framework on natural language inference, where experiments show that enforcing invariants stated in logic can help make the predictions of neural models both accurate and consistent.</abstract>
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%0 Conference Proceedings
%T A Logic-Driven Framework for Consistency of Neural Models
%A Li, Tao
%A Gupta, Vivek
%A Mehta, Maitrey
%A Srikumar, Vivek
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F li-etal-2019-logic
%X While neural models show remarkable accuracy on individual predictions, their internal beliefs can be inconsistent across examples. In this paper, we formalize such inconsistency as a generalization of prediction error. We propose a learning framework for constraining models using logic rules to regularize them away from inconsistency. Our framework can leverage both labeled and unlabeled examples and is directly compatible with off-the-shelf learning schemes without model redesign. We instantiate our framework on natural language inference, where experiments show that enforcing invariants stated in logic can help make the predictions of neural models both accurate and consistent.
%R 10.18653/v1/D19-1405
%U https://aclanthology.org/D19-1405
%U https://doi.org/10.18653/v1/D19-1405
%P 3924-3935
Markdown (Informal)
[A Logic-Driven Framework for Consistency of Neural Models](https://aclanthology.org/D19-1405) (Li et al., EMNLP-IJCNLP 2019)
ACL
- Tao Li, Vivek Gupta, Maitrey Mehta, and Vivek Srikumar. 2019. A Logic-Driven Framework for Consistency of Neural Models. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 3924–3935, Hong Kong, China. Association for Computational Linguistics.