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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 3 4 5 6 7 8...373 Hits 61 – 80 of 7.453 61 The Competitive Advantage of the Indian and Korean Film Industries: An Empirical Analysis Using Natural Language Processing Methods Hyewon Kang; Wenyan Yin; Jinho Kim; Hwy-Chang Moon In: Applied Sciences; Volume 12; Issue 9; Pages: 4592 (2022) BASE Show details 62 Information Processing by Selective Machines Mark Burgin; Karthik Rajagopalan In: Proceedings; Volume 81; Issue 1; Pages: 122 (2022) BASE Show details 63 eHealth Engagement on Facebook during COVID-19: Simplistic Computational Data Analysis Caroll Hermann; Melanie Govender In: International Journal of Environmental Research and Public Health; Volume 19; Issue 8; Pages: 4615 (2022) BASE Show details 64 Cross-Lingual Transfer Learning for Arabic Task-Oriented Dialogue Systems Using Multilingual Transformer Model mT5 Ahlam Fuad; Maha Al-Yahya In: Mathematics; Volume 10; Issue 5; Pages: 746 (2022) BASE Show details 65 Measuring Gender Bias in Contextualized Embeddings Styliani Katsarou; Borja Rodríguez-Gálvez; Jesse Shanahan In: Computer Sciences & Mathematics Forum; Volume 3; Issue 1; Pages: 3 (2022) 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. Keyword: bias detection; contextualized embeddings; deep learning; gender bias; natural language processing URL: https://doi.org/10.3390/cmsf2022003003 BASE Hide details 66 Visual and Phonological Feature Enhanced Siamese BERT for Chinese Spelling Error Correction Yujia Liu; Hongliang Guo; Shuai Wang; Tiejun Wang In: Applied Sciences; Volume 12; Issue 9; Pages: 4578 (2022) BASE Show details 67 AraConv: Developing an Arabic Task-Oriented Dialogue System Using Multi-Lingual Transformer Model mT5 Ahlam Fuad; Maha Al-Yahya In: Applied Sciences; Volume 12; Issue 4; Pages: 1881 (2022) BASE Show details 68 An Empirical Comparison of Portuguese and Multilingual BERT Models for Auto-Classification of NCM Codes in International Trade Roberta Rodrigues de Lima; Anita M. R. Fernandes; James Roberto Bombasar; Bruno Alves da Silva; Paul Crocker; Valderi Reis Quietinho Leithardt In: Big Data and Cognitive Computing; Volume 6; Issue 1; Pages: 8 (2022) BASE Show details 69 Contextual Semantic-Guided Entity-Centric GCN for Relation Extraction Jun Long; Lei Liu; Hongxiao Fei; Yiping Xiang; Haoran Li; Wenti Huang; Liu Yang In: Mathematics; Volume 10; Issue 8; Pages: 1344 (2022) BASE Show details 70 MetaboListem and TABoLiSTM: Two Deep Learning Algorithms for Metabolite Named Entity Recognition Cheng S. Yeung; Tim Beck; Joram M. Posma In: Metabolites; Volume 12; Issue 4; Pages: 276 (2022) BASE Show details 71 Extraction of the Relations among Significant Pharmacological Entities in Russian-Language Reviews of Internet Users on Medications Alexander Sboev; Anton Selivanov; Ivan Moloshnikov; Roman Rybka; Artem Gryaznov; Sanna Sboeva; Gleb Rylkov In: Big Data and Cognitive Computing; Volume 6; Issue 1; Pages: 10 (2022) BASE Show details 72 X-Transformer: A Machine Translation Model Enhanced by the Self-Attention Mechanism Huey-Ing Liu; Wei-Lin Chen In: Applied Sciences; Volume 12; Issue 9; Pages: 4502 (2022) BASE Show details 73 Evaluation of Chinese Natural Language Processing System Based on Metamorphic Testing Lingzi Jin; Zuohua Ding; Huihui Zhou In: Mathematics; Volume 10; Issue 8; Pages: 1276 (2022) BASE Show details 74 Retrieval-Based Transformer Pseudocode Generation Anas Alokla; Walaa Gad; Waleed Nazih; Mustafa Aref; Abdel-Badeeh Salem In: Mathematics; Volume 10; Issue 4; Pages: 604 (2022) BASE Show details 75 An Explainable Fake News Detector Based on Named Entity Recognition and Stance Classification Applied to COVID-19 Giorgio De Magistris; Samuele Russo; Paolo Roma; Janusz T. Starczewski; Christian Napoli In: Information; Volume 13; Issue 3; Pages: 137 (2022) BASE Show details 76 Data of the Shared Task on the Disambiguation of German Verbal Idioms at KONVENS 2021 ... Ehren, Rafael; Lichte, Timm; Waszczuk, Jakub. - : Zenodo, 2022 BASE Show details 77 Data of the Shared Task on the Disambiguation of German Verbal Idioms at KONVENS 2021 ... Ehren, Rafael; Lichte, Timm; Waszczuk, Jakub. - : Zenodo, 2022 BASE Show details 78 Hebrew Transformed: Machine Translation of Hebrew Using the Transformer Architecture Crater, David T. - 2022 BASE Show details 79 Scripted-sentence learning in Spanish speakers (Quique et al., 2022) ... Quique, Yina; Evans, William S.; Ortega-Llebaría, Marta. - : ASHA journals, 2022 BASE Show details 80 Scripted-sentence learning in Spanish speakers (Quique et al., 2022) ... Quique, Yina; Evans, William S.; Ortega-Llebaría, Marta. - : ASHA journals, 2022 BASE Show details Page: 1 2 3 4 5 6 7 8...373

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