Sapphire at SemEval-2022 Task 4: A Patronizing and Condescending Language Detection Model Based on Capsule Networks

Sihui Li, Xiaobing Zhou


Abstract
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.
Anthology ID:
2022.semeval-1.54
Volume:
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
Month:
July
Year:
2022
Address:
Seattle, United States
Editors:
Guy Emerson, Natalie Schluter, Gabriel Stanovsky, Ritesh Kumar, Alexis Palmer, Nathan Schneider, Siddharth Singh, Shyam Ratan
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
405–408
Language:
URL:
https://aclanthology.org/2022.semeval-1.54
DOI:
10.18653/v1/2022.semeval-1.54
Bibkey:
Cite (ACL):
Sihui Li and Xiaobing Zhou. 2022. Sapphire at SemEval-2022 Task 4: A Patronizing and Condescending Language Detection Model Based on Capsule Networks. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 405–408, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
Sapphire at SemEval-2022 Task 4: A Patronizing and Condescending Language Detection Model Based on Capsule Networks (Li & Zhou, SemEval 2022)
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PDF:
https://aclanthology.org/2022.semeval-1.54.pdf