@inproceedings{chhillar-2022-taygete,
title = "Taygete at {S}em{E}val-2022 Task 4: {R}o{BERT}a based models for detecting Patronising and Condescending Language",
author = "Chhillar, Jayant",
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-1.68/",
doi = "10.18653/v1/2022.semeval-1.68",
pages = "496--502",
abstract = "This work describes the development of different models to detect patronising and condescending language within extracts of news articles as part of the SemEval 2022 competition (Task-4). This work explores different models based on the pre-trained RoBERTa language model coupled with LSTM and CNN layers. The best models achieved 15$^{th}$ rank with an F1-score of 0.5924 for subtask-A and 12$^{th}$ in subtask-B with a macro-F1 score of 0.3763."
}
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<abstract>This work describes the development of different models to detect patronising and condescending language within extracts of news articles as part of the SemEval 2022 competition (Task-4). This work explores different models based on the pre-trained RoBERTa language model coupled with LSTM and CNN layers. The best models achieved 15^th rank with an F1-score of 0.5924 for subtask-A and 12^th in subtask-B with a macro-F1 score of 0.3763.</abstract>
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%0 Conference Proceedings
%T Taygete at SemEval-2022 Task 4: RoBERTa based models for detecting Patronising and Condescending Language
%A Chhillar, Jayant
%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 chhillar-2022-taygete
%X This work describes the development of different models to detect patronising and condescending language within extracts of news articles as part of the SemEval 2022 competition (Task-4). This work explores different models based on the pre-trained RoBERTa language model coupled with LSTM and CNN layers. The best models achieved 15^th rank with an F1-score of 0.5924 for subtask-A and 12^th in subtask-B with a macro-F1 score of 0.3763.
%R 10.18653/v1/2022.semeval-1.68
%U https://aclanthology.org/2022.semeval-1.68/
%U https://doi.org/10.18653/v1/2022.semeval-1.68
%P 496-502
Markdown (Informal)
[Taygete at SemEval-2022 Task 4: RoBERTa based models for detecting Patronising and Condescending Language](https://aclanthology.org/2022.semeval-1.68/) (Chhillar, SemEval 2022)
ACL