LCT-1 at SemEval-2023 Task 10: Pre-training and Multi-task Learning for Sexism Detection and Classification

• URL: http://arxiv.org/abs/2306.05075v1
• Date: Thu, 8 Jun 2023 09:56:57 GMT
• Title: LCT-1 at SemEval-2023 Task 10: Pre-training and Multi-task Learning for Sexism Detection and Classification
• Authors: Konstantin Chernyshev, Ekaterina Garanina, Duygu Bayram, Qiankun Zheng, Lukas Edman
• Abstract summary: SemEval-2023 Task 10 on Explainable Detection of Online Sexism aims at increasing explainability of the sexism detection. Our system is based on further domain-adaptive pre-training. In experiments, multi-task learning performs on par with standard fine-tuning for sexism detection.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Misogyny and sexism are growing problems in social media. Advances have been made in online sexism detection but the systems are often uninterpretable. SemEval-2023 Task 10 on Explainable Detection of Online Sexism aims at increasing explainability of the sexism detection, and our team participated in all the proposed subtasks. Our system is based on further domain-adaptive pre-training (Gururangan et al., 2020). Building on the Transformer-based models with the domain adaptation, we compare fine-tuning with multi-task learning and show that each subtask requires a different system configuration. In our experiments, multi-task learning performs on par with standard fine-tuning for sexism detection and noticeably better for coarse-grained sexism classification, while fine-tuning is preferable for fine-grained classification.

Related papers

• Revealing and Reducing Gender Biases in Vision and Language Assistants (VLAs) [82.57490175399693]
  We study gender bias in 22 popular image-to-text vision-language assistants (VLAs) Our results show that VLAs replicate human biases likely present in the data, such as real-world occupational imbalances. To eliminate the gender bias in these models, we find that finetuning-based debiasing methods achieve the best tradeoff between debiasing and retaining performance on downstream tasks.
  arXiv  Detail & Related papers  (2024-10-25T05:59:44Z)
• GenderCARE: A Comprehensive Framework for Assessing and Reducing Gender Bias in Large Language Models [73.23743278545321]
  Large language models (LLMs) have exhibited remarkable capabilities in natural language generation, but have also been observed to magnify societal biases. GenderCARE is a comprehensive framework that encompasses innovative Criteria, bias Assessment, Reduction techniques, and Evaluation metrics.
  arXiv  Detail & Related papers  (2024-08-22T15:35:46Z)
• GenderBias-\emph{VL}: Benchmarking Gender Bias in Vision Language Models via Counterfactual Probing [72.0343083866144]
  This paper introduces the GenderBias-emphVL benchmark to evaluate occupation-related gender bias in Large Vision-Language Models. Using our benchmark, we extensively evaluate 15 commonly used open-source LVLMs and state-of-the-art commercial APIs. Our findings reveal widespread gender biases in existing LVLMs.
  arXiv  Detail & Related papers  (2024-06-30T05:55:15Z)
• A multitask learning framework for leveraging subjectivity of annotators to identify misogyny [47.175010006458436]
  We propose a multitask learning approach to enhance the performance of the misogyny identification systems. We incorporated diverse perspectives from annotators in our model design, considering gender and age across six profile groups. This research advances content moderation and highlights the importance of embracing diverse perspectives to build effective online moderation systems.
  arXiv  Detail & Related papers  (2024-06-22T15:06:08Z)
• Hierarchical Invariance for Robust and Interpretable Vision Tasks at Larger Scales [54.78115855552886]
  We show how to construct over-complete invariants with a Convolutional Neural Networks (CNN)-like hierarchical architecture. With the over-completeness, discriminative features w.r.t. the task can be adaptively formed in a Neural Architecture Search (NAS)-like manner. For robust and interpretable vision tasks at larger scales, hierarchical invariant representation can be considered as an effective alternative to traditional CNN and invariants.
  arXiv  Detail & Related papers  (2024-02-23T16:50:07Z)
• AdamR at SemEval-2023 Task 10: Solving the Class Imbalance Problem in Sexism Detection with Ensemble Learning [1.9833664312284154]
  The Explainable Detection of Online Sexism task presents the problem of explainable sexism detection through fine-grained categorisation of sexist cases. Our team experimented with different ways to combat class imbalance throughout the tasks using data augmentation and loss alteration techniques. This solution ranked us in the top 40% of teams for each of the tracks.
  arXiv  Detail & Related papers  (2023-05-15T13:28:59Z)
• HausaNLP at SemEval-2023 Task 10: Transfer Learning, Synthetic Data and Side-Information for Multi-Level Sexism Classification [0.007696728525672149]
  We present the findings of our participation in the SemEval-2023 Task 10: Explainable Detection of Online Sexism (EDOS) task. We investigated the effects of transferring two language models: XLM-T (sentiment classification) and HateBERT (same domain -- Reddit) for multi-level classification into Sexist or not Sexist.
  arXiv  Detail & Related papers  (2023-04-28T20:03:46Z)
• SemEval-2023 Task 10: Explainable Detection of Online Sexism [5.542286527528687]
  We introduce SemEval Task 10 on the Explainable Detection of Online Sexism (EDOS) We make three main contributions: i) a novel hierarchical taxonomy of sexist content, which includes granular vectors of sexism to aid explainability; ii) a new dataset of 20,000 social media comments with fine-grained labels, along with larger unlabelled datasets for model adaptation; andiii) baseline models as well as an analysis of the methods, results and errors for participant submissions to our task.
  arXiv  Detail & Related papers  (2023-03-07T20:28:39Z)
• Automatic Sexism Detection with Multilingual Transformer Models [0.0]
  This paper presents the contribution of the AIT_FHSTP team at the EXIST 2021 benchmark for two sEXism Identification in Social neTworks tasks. To solve the tasks we applied two multilingual transformer models, one based on multilingual BERT and one based on XLM-R. Our approach uses two different strategies to adapt the transformers to the detection of sexist content: first, unsupervised pre-training with additional data and second, supervised fine-tuning with additional and augmented data. For both tasks our best model is XLM-R with unsupervised pre-training on the EXIST data and additional datasets
  arXiv  Detail & Related papers  (2021-06-09T08:45:51Z)
• Multi-Dimensional Gender Bias Classification [67.65551687580552]
  Machine learning models can inadvertently learn socially undesirable patterns when training on gender biased text. We propose a general framework that decomposes gender bias in text along several pragmatic and semantic dimensions. Using this fine-grained framework, we automatically annotate eight large scale datasets with gender information.
  arXiv  Detail & Related papers  (2020-05-01T21:23:20Z)
• "Call me sexist, but...": Revisiting Sexism Detection Using Psychological Scales and Adversarial Samples [2.029924828197095]
  We outline the different dimensions of sexism by grounding them in their implementation in psychological scales. From the scales, we derive a codebook for sexism in social media, which we use to annotate existing and novel datasets. Results indicate that current machine learning models pick up on a very narrow set of linguistic markers of sexism and do not generalize well to out-of-domain examples.
  arXiv  Detail & Related papers  (2020-04-27T13:07:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.