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Between Deterministic and Nondeterministic Quantitative Automata (Invited Talk)
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Boker, Udi. - : LIPIcs - Leibniz International Proceedings in Informatics. 30th EACSL Annual Conference on Computer Science Logic (CSL 2022), 2022
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Latin Lemmatization & POS Tagging. Issues, Resources, Tools ...
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Latin Lemmatization & POS Tagging. Issues, Resources, Tools ...
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Mehrsprachigkeit und Translanguaging in Migrationsstudien: einige methodologische Überlegungen ; Multilingualism and Translanguaging in Migration Studies: Some Methodological Reflections
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In: Forum Qualitative Sozialforschung / Forum: Qualitative Social Research; Bd. 23 Nr. 1 (2022) ; Forum Qualitative Sozialforschung / Forum: Qualitative Social Research; Vol. 23 No. 1 (2022) ; Forum Qualitative Sozialforschung / Forum: Qualitative Social Research; Vol. 23 Núm. 1 (2022) ; 1438-5627 (2022)
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A Corpus-Based Sentence Classifier for Entity–Relationship Modelling
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In: Electronics; Volume 11; Issue 6; Pages: 889 (2022)
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Text Data Augmentation for the Korean Language
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In: Applied Sciences; Volume 12; Issue 7; Pages: 3425 (2022)
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Connecting Text Classification with Image Classification: A New Preprocessing Method for Implicit Sentiment Text Classification
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In: Sensors; Volume 22; Issue 5; Pages: 1899 (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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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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A Novel Method of Generating Geospatial Intelligence from Social Media Posts of Political Leaders
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In: Information; Volume 13; Issue 3; Pages: 120 (2022)
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Multilingualism and Translanguaging in Migration Studies: Some Methodological Reflections
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In: Forum Qualitative Sozialforschung / Forum: Qualitative Social Research ; 23 ; 1 (2022)
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Detecting weak and strong Islamophobic hate speech on social media
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Maastricht University’s Multilingual Speech Translation System for IWSLT 2021
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Toward Multilingual Neural Machine Translation with Universal Encoder and Decoder
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The Karlsruhe Institute of Technology Systems for the News Translation Task in WMT 2017
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Attention-Passing Models for Robust and Data-Efficient End-to-End Speech Translation
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In: Transactions of the Association for Computational Linguistics, 7, 313–325 ; ISSN: 2307-387X (2022)
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