  Model ensemble techniques often increase task performance in neural networks;
however, they require increased time, memory, and management effort. In this
study, we propose a novel method that replicates the effects of a model
ensemble with a single model. Our approach creates K-virtual models within a
single parameter space using K-distinct pseudo-tags and K-distinct vectors.
Experiments on text classification and sequence labeling tasks on several
datasets demonstrate that our method emulates or outperforms a traditional
model ensemble with 1/K-times fewer parameters.
