@inproceedings{herrmann-krebs-2022-felix,
title = "Felix{\&}Julia at {S}em{E}val-2022 Task 4: Patronizing and Condescending Language Detection",
author = "Herrmann, Felix and
Krebs, Julia",
editor = "Emerson, Guy and
Schluter, Natalie and
Stanovsky, Gabriel and
Kumar, Ritesh and
Palmer, Alexis and
Schneider, Nathan and
Singh, Siddharth and
Ratan, Shyam",
booktitle = "Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.semeval-1.46",
doi = "10.18653/v1/2022.semeval-1.46",
pages = "357--362",
abstract = "This paper describes the authors{'} submission to the SemEval-2022 task 4: Patronizing and Condescending Language (PCL) Detection. The aim of the task is the detection and classification of PCL in an annotated dataset. Subtask 1 includes a binary classification task (PCL or not PCL). Subtask 2 is a multi label classification task where the system identifies different categories of PCL. The authors of this paper submitted two different models: one RoBERTa model and one DistilBERT model. Both systems performed better than the random and RoBERTA baseline given by the task organizers. The RoBERTA model finetuned by the authors performed better in both subtasks than the DistilBERT model.",
}
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<namePart type="given">Felix</namePart>
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<abstract>This paper describes the authors’ submission to the SemEval-2022 task 4: Patronizing and Condescending Language (PCL) Detection. The aim of the task is the detection and classification of PCL in an annotated dataset. Subtask 1 includes a binary classification task (PCL or not PCL). Subtask 2 is a multi label classification task where the system identifies different categories of PCL. The authors of this paper submitted two different models: one RoBERTa model and one DistilBERT model. Both systems performed better than the random and RoBERTA baseline given by the task organizers. The RoBERTA model finetuned by the authors performed better in both subtasks than the DistilBERT model.</abstract>
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%0 Conference Proceedings
%T Felix&Julia at SemEval-2022 Task 4: Patronizing and Condescending Language Detection
%A Herrmann, Felix
%A Krebs, Julia
%Y Emerson, Guy
%Y Schluter, Natalie
%Y Stanovsky, Gabriel
%Y Kumar, Ritesh
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y Singh, Siddharth
%Y Ratan, Shyam
%S Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F herrmann-krebs-2022-felix
%X This paper describes the authors’ submission to the SemEval-2022 task 4: Patronizing and Condescending Language (PCL) Detection. The aim of the task is the detection and classification of PCL in an annotated dataset. Subtask 1 includes a binary classification task (PCL or not PCL). Subtask 2 is a multi label classification task where the system identifies different categories of PCL. The authors of this paper submitted two different models: one RoBERTa model and one DistilBERT model. Both systems performed better than the random and RoBERTA baseline given by the task organizers. The RoBERTA model finetuned by the authors performed better in both subtasks than the DistilBERT model.
%R 10.18653/v1/2022.semeval-1.46
%U https://aclanthology.org/2022.semeval-1.46
%U https://doi.org/10.18653/v1/2022.semeval-1.46
%P 357-362
Markdown (Informal)
[Felix&Julia at SemEval-2022 Task 4: Patronizing and Condescending Language Detection](https://aclanthology.org/2022.semeval-1.46) (Herrmann & Krebs, SemEval 2022)
ACL