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ML_LTU at SemEval-2022 Task 4: T5 Towards Identifying Patronizing and Condescending Language

arXiv.org, 2022-05

2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;http://creativecommons.org/licenses/by/4.0 ;EISSN: 2331-8422 ;DOI: 10.48550/arxiv.2204.07432

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  • Title:
    ML_LTU at SemEval-2022 Task 4: T5 Towards Identifying Patronizing and Condescending Language
  • Author: Adewumi, Tosin ; Alkhaled, Lama ; Hamam Mokayed ; Liwicki, Foteini ; Liwicki, Marcus
  • Subjects: Ablation ; Computer Science - Computation and Language ; Machine learning
  • Is Part Of: arXiv.org, 2022-05
  • Description: 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.
  • Publisher: Ithaca: Cornell University Library, arXiv.org
  • Language: English
  • Identifier: EISSN: 2331-8422
    DOI: 10.48550/arxiv.2204.07432
  • Source: arXiv.org
    AUTh Library subscriptions: ProQuest Central
    Open Access: Freely Accessible Journals by multiple vendors
    ROAD: Directory of Open Access Scholarly Resources
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