  Patronizing and condescending language (PCL) has a large harmful impact and
is difficult to detect, both for human judges and existing NLP systems. At
SemEval-2022 Task 4, we propose a novel Transformer-based model and its
ensembles to accurately understand such language context for PCL detection. To
facilitate comprehension of the subtle and subjective nature of PCL, two
fine-tuning strategies are applied to capture discriminative features from
diverse linguistic behaviour and categorical distribution. The system achieves
remarkable results on the official ranking, including 1st in Subtask 1 and 5th
in Subtask 2. Extensive experiments on the task demonstrate the effectiveness
of our system and its strategies.
