The Pupil Has Become the Master: Teacher-Student Model-Based
Word Embedding Distillation with Ensemble Learning
Abstract
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 highefficient 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
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