SemEval2022-task4: PCL detection

Code repository for the paper:

Xu at SemEval2022 Task 4: pre-BERT Neural Network Methods vs post-BERT RoBERTa Approach for Patronizing and Condescending Language Detection

Author: Jinghua Xu

shared-task

• SemEval2022 Task 4: Patronizing and Condescending Language Detection

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.

code

• run export PYTHONPATH="${PYTHONPATH}:path_to_wd/" in terminal before running each script

data

• upon request, info.