SSN_NLP_MLRG at SemEval-2022 Task 4: Ensemble Learning strategies to detect Patronizing and Condescending Language

Kalaivani Adaikkan, Thenmozhi Durairaj

Abstract
In this paper, we describe our efforts at SemEval 2022 Shared Task 4 on Patronizing and Condescending Language (PCL) Detection. This is the first shared task to detect PCL which is to identify and categorize PCL language towards vulnerable communities. The shared task consists of two subtasks: Patronizing and Condescending language detection (Subtask A) which is the binary task classification and identifying the PCL categories that express the condescension (Subtask B) which is the multi-label text classification. For PCL language detection, We proposed the ensemble strategies of a system combination of BERT, Roberta, Distilbert, Roberta large, Albert achieved the official results for Subtask A with a macro f1 score of 0.5172 on the test set which is improved by baseline score. For PCL Category identification, We proposed a multi-label classification model to ensemble the various Bert-based models and the official results for Subtask B with a macro f1 score of 0.2117 on the test set which is improved by baseline score.
Anthology ID:
2022.semeval-1.53
Volume:
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
SemEval
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
400–404
Language:
URL:
https://aclanthology.org/2022.semeval-1.53
DOI:
10.18653/v1/2022.semeval-1.53
Bibkey:
Cite (ACL):
Kalaivani Adaikkan and Thenmozhi Durairaj. 2022. SSN_NLP_MLRG at SemEval-2022 Task 4: Ensemble Learning strategies to detect Patronizing and Condescending Language. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 400–404, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
SSN_NLP_MLRG at SemEval-2022 Task 4: Ensemble Learning strategies to detect Patronizing and Condescending Language (Adaikkan & Durairaj, SemEval 2022)
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