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

arXiv: https://arxiv.org/abs/2204.07432 · HTML: https://mlsanity.com/paper/2204.07432/

## 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 (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [TalkDown: A Corpus for Condescension Detection in Context](https://mlsanity.com/paper/1909.11272/) (2019)
- [Six Attributes of Unhealthy Conversation](https://mlsanity.com/paper/2010.07410/) (2020)
- [Don't Patronize Me! An Annotated Dataset with Patronizing and Condescending Language towards Vulnerable Communities](https://mlsanity.com/paper/2011.08320/) (2020)
- [Smaaprat: DialoGPT for Natural Language Generation of Swedish Dialogue by Transfer Learning](https://mlsanity.com/paper/2110.06273/) (2021)
- [HaT5: Hate Language Identification using Text-to-Text Transfer Transformer](https://mlsanity.com/paper/2202.05690/) (2022)
- [Predicting Different Types of Subtle Toxicity in Unhealthy Online Conversations](https://mlsanity.com/paper/2106.03952/) (2021)

## Built on by (most cited first, 3 of 3)

- [State-of-the-art in Open-domain Conversational AI: A Survey](https://mlsanity.com/paper/2205.00965/) (2022)
- [Vector Representations of Idioms in Conversational Systems](https://mlsanity.com/paper/2205.03666/) (2022)
- [Masakhane-Afrisenti at SemEval-2023 Task 12: Sentiment Analysis using Afro-centric Language Models and Adapters for Low-resource African Languages](https://mlsanity.com/paper/2304.06459/) (2023)

## 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.