  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.
