  Recent advances in deep learning have facilitated the demand of neural models
for real applications. In practice, these applications often need to be
deployed with limited resources while keeping high accuracy. This paper touches
the core of neural models in NLP, word embeddings, and presents a new embedding
distillation framework that remarkably reduces the dimension of word embeddings
without compromising accuracy. A novel distillation ensemble approach is also
proposed that trains a high-efficient student model using multiple teacher
models. In our approach, the teacher models play roles only during training
such that the student model operates on its own without getting supports from
the teacher models during decoding, which makes it eighty times faster and
lighter than other typical ensemble methods. All models are evaluated on seven
document classification datasets and show a significant advantage over the
teacher models for most cases. Our analysis depicts insightful transformation
of word embeddings from distillation and suggests a future direction to
ensemble approaches using neural models.
