  Domain adaptation approaches seek to learn from a source domain and
generalize it to an unseen target domain. At present, the state-of-the-art
unsupervised domain adaptation approaches for subjective text classification
problems leverage unlabeled target data along with labeled source data. In this
paper, we propose a novel method for domain adaptation of single-task text
classification problems based on a simple but effective idea of diversity-based
generalization that does not require unlabeled target data but still matches
the state-of-the-art in performance. Diversity plays the role of promoting the
model to better generalize and be indiscriminate towards domain shift by
forcing the model not to rely on same features for prediction. We apply this
concept on the most explainable component of neural networks, the attention
layer. To generate sufficient diversity, we create a multi-head attention model
and infuse a diversity constraint between the attention heads such that each
head will learn differently. We further expand upon our model by tri-training
and designing a procedure with an additional diversity constraint between the
attention heads of the tri-trained classifiers. Extensive evaluation using the
standard benchmark dataset of Amazon reviews and a newly constructed dataset of
Crisis events shows that our fully unsupervised method matches with the
competing baselines that uses unlabeled target data. Our results demonstrate
that machine learning architectures that ensure sufficient diversity can
generalize better; encouraging future research to design ubiquitously usable
learning models without using unlabeled target data.
