LeaningTower@LT-EDI-ACL2022: When Hope and Hate Collide
Arianna Muti, Marta Marchiori Manerba, Katerina Korre, Alberto Barrón‐Cedeño · 2022
The 2022 edition of LT-EDI proposed two tasks in various languages.Task hope required models for the automatic identification of hopeful comments for equality, diversity, and inclusion.Task antiLGBT 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 F 1 score of 0.53 for Task hope and 0.46 for Task antiLGBT , placing our team in the third and fourth positions out of 11 and 12 participating teams respectively.