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        <title>Learning Constraints for Structured Prediction Using Rectifier Networks</title>
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        <namePart type="given">Xingyuan</namePart>
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            <namePart type="given">Dan</namePart>
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    <abstract>Various natural language processing tasks are structured prediction problems where outputs are constructed with multiple interdependent decisions. Past work has shown that domain knowledge, framed as constraints over the output space, can help improve predictive accuracy. However, designing good constraints often relies on domain expertise. In this paper, we study the problem of learning such constraints. We frame the problem as that of training a two-layer rectifier network to identify valid structures or substructures, and show a construction for converting a trained network into a system of linear constraints over the inference variables. Our experiments on several NLP tasks show that the learned constraints can improve the prediction accuracy, especially when the number of training examples is small.</abstract>
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%0 Conference Proceedings
%T Learning Constraints for Structured Prediction Using Rectifier Networks
%A Pan, Xingyuan
%A Mehta, Maitrey
%A Srikumar, Vivek
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F pan-etal-2020-learning
%X Various natural language processing tasks are structured prediction problems where outputs are constructed with multiple interdependent decisions. Past work has shown that domain knowledge, framed as constraints over the output space, can help improve predictive accuracy. However, designing good constraints often relies on domain expertise. In this paper, we study the problem of learning such constraints. We frame the problem as that of training a two-layer rectifier network to identify valid structures or substructures, and show a construction for converting a trained network into a system of linear constraints over the inference variables. Our experiments on several NLP tasks show that the learned constraints can improve the prediction accuracy, especially when the number of training examples is small.
%R 10.18653/v1/2020.acl-main.438
%U https://aclanthology.org/2020.acl-main.438
%U https://doi.org/10.18653/v1/2020.acl-main.438
%P 4843-4858

Markdown (Informal)

[Learning Constraints for Structured Prediction Using Rectifier Networks](https://aclanthology.org/2020.acl-main.438) (Pan et al., ACL 2020)

• Learning Constraints for Structured Prediction Using Rectifier Networks (Pan et al., ACL 2020)

ACL

• Xingyuan Pan, Maitrey Mehta, and Vivek Srikumar. 2020. Learning Constraints for Structured Prediction Using Rectifier Networks. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4843–4858, Online. Association for Computational Linguistics.