PCL detection task is aimed at identifying and categorizing language that is
patronizing or condescending towards vulnerable communities in the general
media.Compared to other NLP tasks of paragraph classification, the negative
language presented in the PCL detection task is usually more implicit and
subtle to be recognized, making the performance of common text-classification
approaches disappointed. Targeting the PCL detection problem in SemEval-2022
Task 4, in this paper, we give an introduction to our team's solution, which
exploits the power of prompt-based learning on paragraph classification. We
reformulate the task as an appropriate cloze prompt and use pre-trained Masked
Language Models to fill the cloze slot. For the two subtasks, binary
classification and multi-label classification, DeBERTa model is adopted and
fine-tuned to predict masked label words of task-specific prompts. On the
evaluation dataset, for binary classification, our approach achieves an
F1-score of 0.6406; for multi-label classification, our approach achieves an
macro-F1-score of 0.4689 and ranks first in the leaderboard.

本文探讨了使用基于 prompt 的学习方法解决 PCL 检测问题。使用 DeBERTa 模型和在任务特定提示中自适应预测的方法，将 PCL 检测问题转化为适当的 cloze 提示填充，达到了 0.6406 的二元分类 F1 分数和 0.4689 的多标签分类宏 F1 分数并在 leaderboard 中排名第一的结果。

BEIKE NLP 参加 SemEval-2022 任务 4：基于提示的段落分类，用于辨别居高临下的语言

BEIKE NLP at SemEval-2022 Task 4: Prompt-Based Paragraph Classification for Patronizing and Condescending Language Detection

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.

本文提出了一种基于 Transformer 的模型及其集成，通过两种微调策略捕捉多样的语言行为和分类分布，从而准确理解 PCL 语言语境，获得了出色的结果。