The problem statement addressed in this work is : For a public sentiment classification API, how can we set up a classifier that works well on different types of data, having limited ability to annotate data from across domains. We show that given a large amount of unannotated data from across different domains and pseudolabels on this dataset generated by a classifier trained on a small annotated dataset from one domain, we can train a sentiment classifier that generalizes better across different datasets.
@article{arxiv.2110.02200,
title = {Using Psuedolabels for training Sentiment Classifiers makes the model generalize better across datasets},
author = {Natesh Reddy and Muktabh Mayank Srivastava},
journal= {arXiv preprint arXiv:2110.02200},
year = {2021}
}