Xu at SemEval-2022 Task 4: Pre-BERT Neural Network Methods vs Post-BERT RoBERTa Approach for Patronizing and Condescending Language Detection

TL;DR

This paper compares traditional neural network methods with RoBERTa for detecting patronizing and condescending language, showing that RoBERTa significantly outperforms earlier neural approaches in SemEval-2022 tasks.

Contribution

The study provides an empirical comparison between pre-BERT neural networks and post-BERT RoBERTa models for language detection tasks, highlighting the superiority of RoBERTa.

Findings

RoBERTa outperforms neural network systems in both subtasks.

Top RoBERTa system ranked 26th out of 78 in subtask 1.

RoBERTa achieved an F1-score of 54.64 in subtask 1.

Abstract

This paper describes my participation in the SemEval-2022 Task 4: Patronizing and Condescending Language Detection. I participate in both subtasks: Patronizing and Condescending Language (PCL) Identification and Patronizing and Condescending Language Categorization, with the main focus put on subtask 1. The experiments compare pre-BERT neural network (NN) based systems against post-BERT pretrained language model RoBERTa. This research finds NN-based systems in the experiments perform worse on the task compared to the pretrained language models. The top-performing RoBERTa system is ranked 26 out of 78 teams (F1-score: 54.64) in subtask 1, and 23 out of 49 teams (F1-score: 30.03) in subtask 2.