  Intimacy is an essential element of human relationships and language is a
crucial means of conveying it. Textual intimacy analysis can reveal social
norms in different contexts and serve as a benchmark for testing computational
models' ability to understand social information. In this paper, we propose a
novel weak-labeling strategy for data augmentation in text regression tasks
called WADER. WADER uses data augmentation to address the problems of data
imbalance and data scarcity and provides a method for data augmentation in
cross-lingual, zero-shot tasks. We benchmark the performance of
State-of-the-Art pre-trained multilingual language models using WADER and
analyze the use of sampling techniques to mitigate bias in data and optimally
select augmentation candidates. Our results show that WADER outperforms the
baseline model and provides a direction for mitigating data imbalance and
scarcity in text regression tasks.
