Home Page




Editorial Board

Special Issues

Open Source Software

Proceedings (PMLR)

Transactions (TMLR)




Frequently Asked Questions

Contact Us

RSS Feed

Constraint Reasoning Embedded Structured Prediction

Nan Jiang, Maosen Zhang, Willem-Jan van Hoeve, Yexiang Xue; 23(345):1−40, 2022.


Many real-world structured prediction problems need machine learning to capture data distribution and constraint reasoning to ensure structure validity. Nevertheless, constrained structured prediction is still limited in real-world applications because of the lack of tools to bridge constraint satisfaction and machine learning. In this paper, we propose COnstraint REasoning embedded Structured Prediction (Core-Sp), a scalable constraint reasoning and machine learning integrated approach for learning over structured domains. We propose to embed decision diagrams, a popular constraint reasoning tool, as a fully-differentiable module into deep neural networks for structured prediction. We also propose an iterative search algorithm to automate the searching process of the best Core-Sp structure. We evaluate Core-Sp on three applications: vehicle dispatching service planning, if-then program synthesis, and text2SQL generation. The proposed Core-Sp module demonstrates superior performance over state-of-the-art approaches in all three applications. The structures generated with Core-Sp satisfy 100% of the constraints when using exact decision diagrams. In addition, Core-Sp boosts learning performance by reducing the modeling space via constraint satisfaction.

[abs][pdf][bib]        [code]
© JMLR 2022. (edit, beta)