Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Delta Embedding Learning   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.

15
The categories of this paper are: nucl-ex
13
The categories of this paper are: quant-ph
2
The categories of this paper are: math
7
The categories of this paper are: hep-th
21
The categories of this paper are: adap-org
8
The categories of this paper are: hep-ex
37
The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an
17
The categories of this paper are: q-fin
30
The categories of this paper are: q-alg
3
The categories of this paper are: physics
14
The categories of this paper are: math-ph
33
The categories of this paper are: plasm-ph
18
The categories of this paper are: econ
25
The categories of this paper are: patt-sol
24
The categories of this paper are: dg-ga