LeaningTower@LT-EDI-ACL2022: When Hope and Hate Collide

Abstract

The 2022 edition of LT-EDI proposed two tasks in various languages. Taskhope required models for the automatic identification of hopeful comments for equality, diversity, and inclusion. TaskantiLGBT focused on the identification of homophobic and transphobic comments. We targeted both tasks in English by using reinforced BERT-based approaches. Our core strategy aimed at exploiting the data available for each given task to augment the amount of supervised instances in the other. On the basis of an active learning process, we trained a model on the dataset for Task i and applied it to the dataset for Task j to iteratively integrate new silver data for Task i. Our official submissions to the shared task obtained a macro-averaged F1 score of 0.53 for Taskhope and 0.46 for TaskantiLGBT, placing our team in the third and fourth positions out of 11 and 12 participating teams respectively.