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Intimacy is an essential element of human relationships and language is acrucial means of conveying it. Textual intimacy analysis can reveal socialnorms in different contexts and serve as a benchmark for testing computationalmodels' ability to understand social information. In this paper, we propose anovel weak-labeling strategy for data augmentation in text regression taskscalled WADER. WADER uses data augmentation to address the problems of dataimbalance and data scarcity and provides a method for data augmentation incross-lingual, zero-shot tasks. We benchmark the performance ofState-of-the-Art pre-trained multilingual language models using WADER andanalyze the use of sampling techniques to mitigate bias in data and optimallyselect augmentation candidates. Our results show that WADER outperforms thebaseline model and provides a direction for mitigating data imbalance andscarcity in text regression tasks.

WADER at SemEval-2023 Task 9: A Weak-labelling framework for Data augmentation in tExt Regression Tasks