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Новые данные о речевом дыхании по результатам онлайновой магнитно-резонансной томографии легких ... : New data on speech breathing upon results from online Magnetic Resonance Imaging ...
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| 26 |
Appendix: Training Multilingual Writing Strategies in Higher Education ... : Anhang zu Training Multilingual Writing Strategies in Higher Education ...
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Interference in processing of Czech intraclausal garden-path structures - yes/no questions ...
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Agreement attraction in English and Czech: A direct experimental comparison ...
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Interference in processing of Czech intraclausal garden-path structures - open-ended questions ...
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| 32 |
Current & Future Research Directions in Singapore Mandarin ...
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| 33 |
Use of Parsing Heuristics in the Comprehension of Passive Sentences: Evidence from Dyslexia and Individual Differences
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In: Brain Sciences; Volume 12; Issue 2; Pages: 209 (2022)
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| 34 |
What Is Going on with Visual Attention in Reading and Dyslexia? A Critical Review of Recent Studies
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In: Brain Sciences; Volume 12; Issue 1; Pages: 87 (2022)
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| 35 |
Can Heritage Speakers Predict Lexical and Morphosyntactic Information in Reading?
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In: Languages; Volume 7; Issue 1; Pages: 60 (2022)
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| 36 |
Multimodal Lip-Reading for Tracheostomy Patients in the Greek Language
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In: Computers; Volume 11; Issue 3; Pages: 34 (2022)
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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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Abstract:
Machine reading comprehension (MRC) of text data is a challenging task in Natural Language Processing (NLP), with a lot of ongoing research fueled by the release of the Stanford Question Answering Dataset (SQuAD) and Conversational Question Answering (CoQA). It is considered to be an effort to teach computers how to “understand” a text, and then to be able to answer questions about it using deep learning. However, until now, large-scale training on private text data and knowledge sharing has been missing for this NLP task. Hence, we present FedQAS, a privacy-preserving machine reading system capable of leveraging large-scale private data without the need to pool those datasets in a central location. The proposed approach combines transformer models and federated learning technologies. The system is developed using the FEDn framework and deployed as a proof-of-concept alliance initiative. FedQAS is flexible, language-agnostic, and allows intuitive participation and execution of local model training. In addition, we present the architecture and implementation of the system, as well as provide a reference evaluation based on the SQuAD dataset, to showcase how it overcomes data privacy issues and enables knowledge sharing between alliance members in a Federated learning setting.
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Keyword:
data privacy; federated learning; machine reading comprehension; natural language processing; question answering; transformer
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URL: https://doi.org/10.3390/app12063130
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| 38 |
Beyond the Edge: Markerless Pose Estimation of Speech Articulators from Ultrasound and Camera Images Using DeepLabCut
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In: Sensors; Volume 22; Issue 3; Pages: 1133 (2022)
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| 39 |
Literacy Acquisition Trajectories in Bilingual Language Minority Children and Monolingual Peers with Similar or Different SES: A Three-Year Longitudinal Study
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In: Brain Sciences; Volume 12; Issue 5; Pages: 563 (2022)
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Remote Dyslexia Screening for Bilingual Children
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In: Multimodal Technologies and Interaction; Volume 6; Issue 1; Pages: 7 (2022)
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