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| 61 |
FedQAS: Privacy-Aware Machine Reading Comprehension with Federated Learning
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In: Applied Sciences; Volume 12; Issue 6; Pages: 3130 (2022)
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| 62 |
A Dynamic Attention and Multi-Strategy-Matching Neural Network Based on Bert for Chinese Rice-Related Answer Selection
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In: Agriculture; Volume 12; Issue 2; Pages: 176 (2022)
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| 63 |
Correcting Diacritics and Typos with a ByT5 Transformer Model
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In: Applied Sciences; Volume 12; Issue 5; Pages: 2636 (2022)
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| 64 |
The Competitive Advantage of the Indian and Korean Film Industries: An Empirical Analysis Using Natural Language Processing Methods
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In: Applied Sciences; Volume 12; Issue 9; Pages: 4592 (2022)
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| 65 |
Information Processing by Selective Machines
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In: Proceedings; Volume 81; Issue 1; Pages: 122 (2022)
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| 66 |
eHealth Engagement on Facebook during COVID-19: Simplistic Computational Data Analysis
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In: International Journal of Environmental Research and Public Health; Volume 19; Issue 8; Pages: 4615 (2022)
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| 67 |
Cross-Lingual Transfer Learning for Arabic Task-Oriented Dialogue Systems Using Multilingual Transformer Model mT5
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In: Mathematics; Volume 10; Issue 5; Pages: 746 (2022)
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| 68 |
Measuring Gender Bias in Contextualized Embeddings
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In: Computer Sciences & Mathematics Forum; Volume 3; Issue 1; Pages: 3 (2022)
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Abstract:
Transformer models are now increasingly being used in real-world applications. Indiscriminately using these models as automated tools may propagate biases in ways we do not realize. To responsibly direct actions that will combat this problem, it is of crucial importance that we detect and quantify these biases. Robust methods have been developed to measure bias in non-contextualized embeddings. Nevertheless, these methods fail to apply to contextualized embeddings due to their mutable nature. Our study focuses on the detection and measurement of stereotypical biases associated with gender in the embeddings of T5 and mT5. We quantify bias by measuring the gender polarity of T5’s word embeddings for various professions. To measure gender polarity, we use a stable gender direction that we detect in the model’s embedding space. We also measure gender bias with respect to a specific downstream task and compare Swedish with English, as well as various sizes of the T5 model and its multilingual variant. The insights from our exploration indicate that the use of a stable gender direction, even in a Transformer’s mutable embedding space, can be a robust method to measure bias. We show that higher status professions are associated more with the male gender than the female gender. In addition, our method suggests that the Swedish language carries less bias associated with gender than English, and the higher manifestation of gender bias is associated with the use of larger language models.
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Keyword:
bias detection; contextualized embeddings; deep learning; gender bias; natural language processing
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URL: https://doi.org/10.3390/cmsf2022003003
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| 69 |
Comparative Study of Multiclass Text Classification in Research Proposals Using Pretrained Language Models
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In: Applied Sciences; Volume 12; Issue 9; Pages: 4522 (2022)
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| 70 |
Visual and Phonological Feature Enhanced Siamese BERT for Chinese Spelling Error Correction
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In: Applied Sciences; Volume 12; Issue 9; Pages: 4578 (2022)
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| 71 |
AraConv: Developing an Arabic Task-Oriented Dialogue System Using Multi-Lingual Transformer Model mT5
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In: Applied Sciences; Volume 12; Issue 4; Pages: 1881 (2022)
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| 72 |
An Empirical Comparison of Portuguese and Multilingual BERT Models for Auto-Classification of NCM Codes in International Trade
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In: Big Data and Cognitive Computing; Volume 6; Issue 1; Pages: 8 (2022)
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| 73 |
Contextual Semantic-Guided Entity-Centric GCN for Relation Extraction
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In: Mathematics; Volume 10; Issue 8; Pages: 1344 (2022)
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| 74 |
MetaboListem and TABoLiSTM: Two Deep Learning Algorithms for Metabolite Named Entity Recognition
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In: Metabolites; Volume 12; Issue 4; Pages: 276 (2022)
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| 75 |
Extraction of the Relations among Significant Pharmacological Entities in Russian-Language Reviews of Internet Users on Medications
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In: Big Data and Cognitive Computing; Volume 6; Issue 1; Pages: 10 (2022)
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| 76 |
X-Transformer: A Machine Translation Model Enhanced by the Self-Attention Mechanism
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In: Applied Sciences; Volume 12; Issue 9; Pages: 4502 (2022)
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| 77 |
Evaluation of Chinese Natural Language Processing System Based on Metamorphic Testing
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In: Mathematics; Volume 10; Issue 8; Pages: 1276 (2022)
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| 78 |
Retrieval-Based Transformer Pseudocode Generation
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In: Mathematics; Volume 10; Issue 4; Pages: 604 (2022)
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| 79 |
An Explainable Fake News Detector Based on Named Entity Recognition and Stance Classification Applied to COVID-19
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In: Information; Volume 13; Issue 3; Pages: 137 (2022)
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| 80 |
Data of the Shared Task on the Disambiguation of German Verbal Idioms at KONVENS 2021 ...
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