Distilling Word Embeddings: An Encoding Approach

Lili Mou; Ran Jia; Yan Xu; Ge Li; Lu Zhang; Zhi Jin

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arXiv · 1506.04488

Distilling Word Embeddings: An Encoding Approach

Lili Mou·Ran Jia·Yan Xu·Ge Li·Lu Zhang·Zhi Jin

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Abstract

Distilling knowledge from a well-trained cumbersome network to a small one has recently become a new research topic, as lightweight neural networks with high performance are particularly in need in various resource-restricted systems. This paper addresses the problem of distilling word embeddings for NLP tasks. We propose an encoding approach to distill task-specific knowledge from a set of high-dimensional embeddings, which can reduce model complexity by a large margin as well as retain high accuracy, showing a good compromise between efficiency and performance. Experiments in two tasks reveal the phenomenon that distilling knowledge from cumbersome embeddings is better than directly training neural networks with small embeddings.

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Lili Mou, Ran Jia, Yan Xu, Ge Li, Lu Zhang, Zhi Jin. 2015-06-15. Distilling Word Embeddings: An Encoding Approach. https://arxiv.org/abs/1506.04488

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