Sapphire at SemEval-2022 Task 4: A Patronizing and Condescending Language Detection Model Based on Capsule Networks
Sihui Li, Xiaobing Zhou · Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) · 2022
This paper introduces the related work and the results of Team Sapphire's system for SemEval-2022 Task 4: Patronizing and Condescending Language Detection.We only participated in subtask 1.The task goal is to judge whether a news text contains PCL.This task can be considered as a task of binary classification of news texts.In this binary classification task, the BERT-base model is adopted as the pre-trained model used to represent textual information in vector form and encode it.Capsule networks is adopted to extract features from the encoded vectors.The official evaluation metric for subtask 1 is the F1 score over the positive class.Finally, our system's submitted prediction results on test set achieved the score of 0.5187.