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MCSQ Translation Models (en-ru) (v1.0)
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Variš, Dušan. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2022
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MCSQ Translation Models (en-de) (v1.0)
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Variš, Dušan. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2022
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Evaluation computergestützter Verfahren der Emotionsklassifikation für deutschsprachige Dramen um 1800 ...
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Evaluation computergestützter Verfahren der Emotionsklassifikation für deutschsprachige Dramen um 1800 ...
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Transformer-Based Abstractive Summarization for Reddit and Twitter: Single Posts vs. Comment Pools in Three Languages
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In: Future Internet; Volume 14; Issue 3; Pages: 69 (2022)
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Speech Enhancement by Multiple Propagation through the Same Neural Network
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In: Sensors; Volume 22; Issue 7; Pages: 2440 (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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A Pipeline Approach to Context-Aware Handwritten Text Recognition
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In: Applied Sciences; Volume 12; Issue 4; Pages: 1870 (2022)
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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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Research on Named Entity Recognition Methods in Chinese Forest Disease Texts
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In: Applied Sciences; Volume 12; Issue 8; Pages: 3885 (2022)
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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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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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Retrieval-Based Transformer Pseudocode Generation
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In: Mathematics; Volume 10; Issue 4; Pages: 604 (2022)
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Hebrew Transformed: Machine Translation of Hebrew Using the Transformer Architecture
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Abstract:
This thesis presents the first known end-to-end application to Hebrew language of Google’s state-of-the-art Transformer architecture for natural language processing (NLP). The state of the art in machine translation (MT) of Hebrew remains poor. Scholarly work in MT, deep learning (DL), and other areas of NLP for Hebrew began to develop much later and remains much less mature than for other languages. The problem is difficult because of the nature of Hebrew as a morphologically-rich language (MRL), the small size of the total corpus of electronic Hebrew documents available as training material, and the small size of the Hebrew-literate computing community worldwide. Nonetheless, significant advances in Hebrew NLP tools, data, methods, and scholarly infrastructure over the last 15 years, combined with recent advances in general NLP and MT over the last few years, especially the rise of neural networks and deep learning, create an enticing opportunity to attempt to advance the current state of Hebrew MT. More specifically, Google’s Transformer neural network and associated technologies such as bidirectional encoder representations from Transformers (BERT) have revolutionized general MT and hold great promise for improving automatic Hebrew translation. This thesis demonstrates that, as measured by METEOR scores, a basic Hebrew Transformer trained in a few hours on a single GPU (graphics processing unit) exceeds the current performance of Google Translate on in-genre Hebrew translation tasks and is not far behind Google Translate on Hebrew translation tasks in general.
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Keyword:
Artificial intelligence; bidirectional encoder representations from transformers (BERT); computational linguistics; Computer science; hebrew; Linguistics; machine translation; natural language processing (NLP); transformer
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URL: https://nrs.harvard.edu/URN-3:HUL.INSTREPOS:37370749
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English machine reading comprehension: new approaches to answering multiple-choice questions
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Dzendzik, Daria. - : Dublin City University. School of Computing, 2021. : Dublin City University. ADAPT, 2021
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In: Dzendzik, Daria (2021) English machine reading comprehension: new approaches to answering multiple-choice questions. PhD thesis, Dublin City University. (2021)
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Transformer versus LSTM Language Models Trained on Uncertain ASR Hypotheses in Limited Data Scenarios
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In: https://hal.inria.fr/hal-03362828 ; 2021 (2021)
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Simulating reading mistakes for child speech Transformer-based phone recognition
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In: Annual Conference of the International Speech Communication Association (INTERSPEECH) ; https://hal.archives-ouvertes.fr/hal-03257870 ; Annual Conference of the International Speech Communication Association (INTERSPEECH), Aug 2021, Brno, Czech Republic (2021)
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Breaking Down the Invisible Wall of Informal Fallacies in Online Discussions
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In: ACL-IJCNLP 2021 - Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing ; https://hal.inria.fr/hal-03351649 ; ACL-IJCNLP 2021 - Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, Aug 2021, Online, France ; https://2021.aclweb.org/ (2021)
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End-to-end acoustic modelling for phone recognition of young readers
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In: ISSN: 0167-6393 ; EISSN: 1872-7182 ; Speech Communication ; https://hal.archives-ouvertes.fr/hal-03373156 ; Speech Communication, Elsevier : North-Holland, 2021, 134, pp.71-84. ⟨10.1016/j.specom.2021.08.003⟩ ; https://www.sciencedirect.com/science/article/pii/S0167639321000959?via%3Dihub (2021)
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Multitask Transformer Model-based Fintech Customer Service Chatbot NLU System with DECO-LGG SSP-based Data ; DECO-LGG 반자동 증강 학습데이터 활용 멀티태스크 트랜스포머 모델 기반 핀테크 CS 챗봇 NLU 시스템
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In: Annual Conference on Human and Language Technology ; https://hal.archives-ouvertes.fr/hal-03603903 ; Annual Conference on Human and Language Technology, Oct 2021, Séoul, South Korea. pp.461-466 ; http://www.koreascience.or.kr/journal/OOGHAK.page (2021)
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