mlsanityData to 14 Oct 2026

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

arXiv 2204.07432 ↗First posted 15 Apr 2022cs.CLcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 9 of its 17 references that are in the map. First posted on arXiv 15 Apr 2022.

Builds on

9 of 17 references.

References · 9

  1. 2017Attention Is All You Needcited by 60,361
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  3. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,548
  4. 2019TalkDown: A Corpus for Condescension Detection in Contextcited by 21
  5. 2020Six Attributes of Unhealthy Conversationcited by 19
  6. 2020Don't Patronize Me! An Annotated Dataset with Patronizing and Condescending Language towards Vulnerable Communitiescited by 19
  7. 2021Smaaprat: DialoGPT for Natural Language Generation of Swedish Dialogue by Transfer Learningcited by 11
  8. 2022HaT5: Hate Language Identification using Text-to-Text Transfer Transformercited by 8
  9. 2021Predicting Different Types of Subtle Toxicity in Unhealthy Online Conversationscited by 5

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Citing papers per year: 2022: 2; 2023: 1.

3 papers.