title: Enforcing constraints on outputs with unconstrained inference
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.
authors: Jay Yoon Lee Michael L. Wick Jean-Baptiste Tristan
hIndex: Michael L. Wick 19