Delta Embedding Learning

Xiao Zhang; Ji Wu; Dejing Dou

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arXiv · 1812.04160

Delta Embedding Learning

Xiao Zhang·Ji Wu·Dejing Dou

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Abstract

Unsupervised word embeddings have become a popular approach of word representation in NLP tasks. However there are limitations to the semantics represented by unsupervised embeddings, and inadequate fine-tuning of embeddings can lead to suboptimal performance. We propose a novel learning technique called Delta Embedding Learning, which can be applied to general NLP tasks to improve performance by optimized tuning of the word embeddings. A structured regularization is applied to the embeddings to ensure they are tuned in an incremental way. As a result, the tuned word embeddings become better word representations by absorbing semantic information from supervision without "forgetting." We apply the method to various NLP tasks and see a consistent improvement in performance. Evaluation also confirms the tuned word embeddings have better semantic properties.

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BibTeX RIS

Xiao Zhang, Ji Wu, Dejing Dou. 2018-12-11. Delta Embedding Learning. https://arxiv.org/abs/1812.04160

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