Unsupervised Domain Adaptation using Lexical Transformations and Label
  Injection for Twitter Data
Akshat Gupta Xiaomo Liu Sameena Shah
Domain adaptation is an important and widely studied problem in natural
language processing. A large body of literature tries to solve this problem by
adapting models trained on the source domain to the target domain. In this
paper, we instead solve this problem from a dataset perspective. We modify the
source domain dataset with simple lexical transformations to reduce the domain
shift between the source dataset distribution and the target dataset
distribution. We find that models trained on the transformed source domain
dataset performs significantly better than zero-shot models. Using our proposed
transformations to convert standard English to tweets, we reach an unsupervised
part-of-speech (POS) tagging accuracy of 92.14% (from 81.54% zero shot
accuracy), which is only slightly below the supervised performance of 94.45%.
We also use our proposed transformations to synthetically generate tweets and
augment the Twitter dataset to achieve state-of-the-art performance for POS
tagging.