@inproceedings{adewumi-etal-2022-ml,
title = "{ML}{\_}{LTU} at {S}em{E}val-2022 Task 4: T5 Towards Identifying Patronizing and Condescending Language",
author = "Adewumi, Tosin and
Alkhaled, Lama and
Mokayed, Hamam and
Liwicki, Foteini and
Liwicki, Marcus",
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-1.64",
doi = "10.18653/v1/2022.semeval-1.64",
pages = "473--478",
abstract = "This paper describes the system used by the Machine Learning Group of LTU in subtask 1 of the SemEval-2022 Task 4: Patronizing and Condescending Language (PCL) Detection. Our system consists of finetuning a pretrained text-to-text transfer transformer (T5) and innovatively reducing its out-of-class predictions. The main contributions of this paper are 1) the description of the implementation details of the T5 model we used, 2) analysis of the successes {\&} struggles of the model in this task, and 3) ablation studies beyond the official submission to ascertain the relative importance of data split. Our model achieves an F1 score of 0.5452 on the official test set.",
}
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<abstract>This paper describes the system used by the Machine Learning Group of LTU in subtask 1 of the SemEval-2022 Task 4: Patronizing and Condescending Language (PCL) Detection. Our system consists of finetuning a pretrained text-to-text transfer transformer (T5) and innovatively reducing its out-of-class predictions. The main contributions of this paper are 1) the description of the implementation details of the T5 model we used, 2) analysis of the successes & struggles of the model in this task, and 3) ablation studies beyond the official submission to ascertain the relative importance of data split. Our model achieves an F1 score of 0.5452 on the official test set.</abstract>
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%0 Conference Proceedings
%T ML_LTU at SemEval-2022 Task 4: T5 Towards Identifying Patronizing and Condescending Language
%A Adewumi, Tosin
%A Alkhaled, Lama
%A Mokayed, Hamam
%A Liwicki, Foteini
%A Liwicki, Marcus
%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 adewumi-etal-2022-ml
%X This paper describes the system used by the Machine Learning Group of LTU in subtask 1 of the SemEval-2022 Task 4: Patronizing and Condescending Language (PCL) Detection. Our system consists of finetuning a pretrained text-to-text transfer transformer (T5) and innovatively reducing its out-of-class predictions. The main contributions of this paper are 1) the description of the implementation details of the T5 model we used, 2) analysis of the successes & struggles of the model in this task, and 3) ablation studies beyond the official submission to ascertain the relative importance of data split. Our model achieves an F1 score of 0.5452 on the official test set.
%R 10.18653/v1/2022.semeval-1.64
%U https://aclanthology.org/2022.semeval-1.64
%U https://doi.org/10.18653/v1/2022.semeval-1.64
%P 473-478
Markdown (Informal)
[ML_LTU at SemEval-2022 Task 4: T5 Towards Identifying Patronizing and Condescending Language](https://aclanthology.org/2022.semeval-1.64) (Adewumi et al., SemEval 2022)
ACL