Enforcing constraints on outputs with unconstrained inference

Jay Yoon Lee, Michael L. Wick, Jean-Baptiste Tristan

Nov 03, 2016 (modified: Nov 08, 2016) ICLR 2017 conference submission readers: everyone
  • Abstract: Increasingly, practitioners apply neural networks to complex problems in natural language processing (NLP), such as syntactic parsing, that have rich output structures. Many such applications require deterministic constraints on the output values; for example, requiring that the sequential outputs encode a valid tree. While hidden units might capture such properties, the network is not always able to learn them from the training data alone, and practitioners must then resort to post-processing. In this paper, we present an inference method for neural networks that enforces deterministic constraints on outputs without performing post-processing or expensive discrete search over the feasible space. Instead, for each input, we nudge the continuous weights until the network's unconstrained inference procedure generates an output that satisfies the constraints. We find that our method reduces the number of violating outputs by up to 81\%, while improving accuracy.
  • TL;DR: An inference method for enforcing hard constraints on the outputs of neural networks without combinatorial search, with applications in NLP and structured prediction.
  • Keywords: Natural language processing, Structured prediction, Deep learning
  • Conflicts: cs.umass.edu, oracle.com, cmu.edu, harvard.edu

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