PALI-NLP at SemEval-2022 Task 4: Discriminative Fine-tuning of Deep Transformers for Patronizing and Condescending Language Detection

• URL: http://arxiv.org/abs/2203.04616v1
• Date: Wed, 9 Mar 2022 10:05:10 GMT
• Title: PALI-NLP at SemEval-2022 Task 4: Discriminative Fine-tuning of Deep Transformers for Patronizing and Condescending Language Detection
• Authors: Dou Hu, Mengyuan Zhou, Xiyang Du, Mengfei Yuan, Meizhi Jin, Lianxin Jiang, Yang Mo, Xiaofeng Shi
• Abstract summary: 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. The system achieves remarkable results on the official ranking, namely 1st in Subtask 1 and 5th in Subtask 2.
• Score: 4.883341580669763
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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, namely 1st in Subtask 1 and 5th in Subtask 2. Extensive experiments on the task demonstrate the effectiveness of our system and its strategies.

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