@inproceedings{verma-etal-2023-dcu,
title = "{DCU} at {S}em{E}val-2023 Task 10: A Comparative Analysis of Encoder-only and Decoder-only Language Models with Insights into Interpretability",
author = "Verma, Kanishk and
Adebayo, Kolawole and
Wagner, Joachim and
Davis, Brian",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Da San Martino, Giovanni and
Tayyar Madabushi, Harish and
Kumar, Ritesh and
Sartori, Elisa},
booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.semeval-1.241",
doi = "10.18653/v1/2023.semeval-1.241",
pages = "1736--1750",
abstract = "We conduct a comparison of pre-trained encoder-only and decoder-only language models with and without continued pre-training, to detect online sexism. Our fine-tuning-based classifier system achieved the 16th rank in the SemEval 2023 Shared Task 10 Subtask A that asks to distinguish sexist and non-sexist texts. Additionally, we conduct experiments aimed at enhancing the interpretability of systems designed to detect online sexism. Our findings provide insights into the features and decision-making processes underlying our classifier system, thereby contributing to a broader effort to develop explainable AI models to detect online sexism.",
}
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%0 Conference Proceedings
%T DCU at SemEval-2023 Task 10: A Comparative Analysis of Encoder-only and Decoder-only Language Models with Insights into Interpretability
%A Verma, Kanishk
%A Adebayo, Kolawole
%A Wagner, Joachim
%A Davis, Brian
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Da San Martino, Giovanni
%Y Tayyar Madabushi, Harish
%Y Kumar, Ritesh
%Y Sartori, Elisa
%S Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F verma-etal-2023-dcu
%X We conduct a comparison of pre-trained encoder-only and decoder-only language models with and without continued pre-training, to detect online sexism. Our fine-tuning-based classifier system achieved the 16th rank in the SemEval 2023 Shared Task 10 Subtask A that asks to distinguish sexist and non-sexist texts. Additionally, we conduct experiments aimed at enhancing the interpretability of systems designed to detect online sexism. Our findings provide insights into the features and decision-making processes underlying our classifier system, thereby contributing to a broader effort to develop explainable AI models to detect online sexism.
%R 10.18653/v1/2023.semeval-1.241
%U https://aclanthology.org/2023.semeval-1.241
%U https://doi.org/10.18653/v1/2023.semeval-1.241
%P 1736-1750
Markdown (Informal)
[DCU at SemEval-2023 Task 10: A Comparative Analysis of Encoder-only and Decoder-only Language Models with Insights into Interpretability](https://aclanthology.org/2023.semeval-1.241) (Verma et al., SemEval 2023)
ACL