Taygete at SemEval-2022 Task 4: RoBERTa based models for detecting Patronising and Condescending Language

04/22/2022
by   Jayant Chhillar, et al.
0

This work describes the development of different models to detect patronising and condescending language within extracts of news articles as part of the SemEval 2022 competition (Task-4). This work explores different models based on the pre-trained RoBERTa language model coupled with LSTM and CNN layers. The best models achieved 15^th rank with an F1-score of 0.5924 for subtask-A and 12^th in subtask-B with a macro-F1 score of 0.3763.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
08/24/2020

YNU-HPCC at SemEval-2020 Task 11: LSTM Network for Detection of Propaganda Techniques in News Articles

This paper summarizes our studies on propaganda detection techniques for...
research
01/23/2019

Self-Attentive Model for Headline Generation

Headline generation is a special type of text summarization task. While ...
research
01/26/2022

FiNCAT: Financial Numeral Claim Analysis Tool

While making investment decisions by reading financial documents, invest...
research
08/29/2020

SocCogCom at SemEval-2020 Task 11: Characterizing and Detecting Propaganda using Sentence-Level Emotional Salience Features

This paper describes a system developed for detecting propaganda techniq...
research
08/31/2023

Link Prediction for Wikipedia Articles as a Natural Language Inference Task

Link prediction task is vital to automatically understanding the structu...
research
12/31/2021

Hypers at ComMA@ICON: Modelling Aggressiveness, Gender Bias and Communal Bias Identification

Due to the exponentially increasing reach of social media, it is essenti...
research
03/22/2021

Identifying Machine-Paraphrased Plagiarism

Employing paraphrasing tools to conceal plagiarized text is a severe thr...

Please sign up or login with your details

Forgot password? Click here to reset