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As large Pre-trained Language Models (PLMs) trained on large amounts of datain an unsupervised manner become more ubiquitous, identifying various types ofbias in the text has come into sharp focus. Existing "Stereotype Detection"datasets mainly adopt a diagnostic approach toward large PLMs. Blodgett et. al(2021a) show that there are significant reliability issues with the existingbenchmark datasets. Annotating a reliable dataset requires a preciseunderstanding of the subtle nuances of how stereotypes manifest in text. Inthis paper, we annotate a focused evaluation set for "Stereotype Detection"that addresses those pitfalls by de-constructing various ways in whichstereotypes manifest in text. Further, we present a multi-task model thatleverages the abundance of data-rich neighboring tasks such as hate speechdetection, offensive language detection, misogyny detection, etc., to improvethe empirical performance on "Stereotype Detection". We then propose areinforcement-learning agent that guides the multi-task learning model bylearning to identify the training examples from the neighboring tasks that helpthe target task the most. We show that the proposed models achieve significantempirical gains over existing baselines on all the tasks.

Reinforcement Guided Multi-Task Learning Framework for Low-Resource Stereotype Detection