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    <abstract>As social media platforms grow, so too does the volume of hate speech and negative sentiment expressed towards particular social groups. In this paper, we describe our approach to SemEval-2023 Task 10, involving the detection and classification of online sexism (abuse directed towards women), with fine-grained categorisations intended to facilitate the development of a more nuanced understanding of the ideologies and processes through which online sexism is expressed. We experiment with several approaches involving language model finetuning, class-specific adapters, and pseudo-labelling. Our best-performing models involve the training of adapters specific to each subtask category (combined via fusion layers) using a weighted loss function, in addition to performing naive pseudo-labelling on a large quantity of unlabelled data. We successfully outperform the baseline models on all 3 subtasks, placing 56th (of 84) on Task A, 43rd (of 69) on Task B,and 37th (of 63) on Task C.</abstract>
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%0 Conference Proceedings
%T shefnlp at SemEval-2023 Task 10: Compute-Efficient Category Adapters
%A Pickard, Thomas
%A Loakman, Tyler
%A Pandya, Mugdha
%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 pickard-etal-2023-shefnlp
%X As social media platforms grow, so too does the volume of hate speech and negative sentiment expressed towards particular social groups. In this paper, we describe our approach to SemEval-2023 Task 10, involving the detection and classification of online sexism (abuse directed towards women), with fine-grained categorisations intended to facilitate the development of a more nuanced understanding of the ideologies and processes through which online sexism is expressed. We experiment with several approaches involving language model finetuning, class-specific adapters, and pseudo-labelling. Our best-performing models involve the training of adapters specific to each subtask category (combined via fusion layers) using a weighted loss function, in addition to performing naive pseudo-labelling on a large quantity of unlabelled data. We successfully outperform the baseline models on all 3 subtasks, placing 56th (of 84) on Task A, 43rd (of 69) on Task B,and 37th (of 63) on Task C.
%R 10.18653/v1/2023.semeval-1.147
%U https://aclanthology.org/2023.semeval-1.147
%U https://doi.org/10.18653/v1/2023.semeval-1.147
%P 1069-1075

Markdown (Informal)

[shefnlp at SemEval-2023 Task 10: Compute-Efficient Category Adapters](https://aclanthology.org/2023.semeval-1.147) (Pickard et al., SemEval 2023)

• shefnlp at SemEval-2023 Task 10: Compute-Efficient Category Adapters (Pickard et al., SemEval 2023)

ACL

• Thomas Pickard, Tyler Loakman, and Mugdha Pandya. 2023. shefnlp at SemEval-2023 Task 10: Compute-Efficient Category Adapters. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 1069–1075, Toronto, Canada. Association for Computational Linguistics.