Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Constraints for Structured Prediction Using Rectifier Networks   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.

28
The categories of this paper are: alg-geom
6
The categories of this paper are: astro-ph
14
The categories of this paper are: math-ph
9
The categories of this paper are: nlin
25
The categories of this paper are: patt-sol
29
The categories of this paper are: funct-an
23
The categories of this paper are: solv-int
0
The categories of this paper are: hep-ph
22
The categories of this paper are: chao-dyn
31
The categories of this paper are: ao-sci
27
The categories of this paper are: comp-gas
16
The categories of this paper are: stat
35
The categories of this paper are: mtrl-th
15
The categories of this paper are: nucl-ex
18
The categories of this paper are: econ
11
The categories of this paper are: hep-lat