Using Psuedolabels for training Sentiment Classifiers makes the model generalize better across datasets

• URL: http://arxiv.org/abs/2110.02200v1
• Date: Tue, 5 Oct 2021 17:47:15 GMT
• Title: Using Psuedolabels for training Sentiment Classifiers makes the model generalize better across datasets
• Authors: Natesh Reddy, Muktabh Mayank Srivastava
• Abstract summary: 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, we can train a sentiment classifier that generalizes better across different datasets.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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