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Linguistic Mathematical Relationships Saved or Lost in Translating Texts: Extension of the Statistical Theory of Translation and Its Application to the New Testament
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In: Information; Volume 13; Issue 1; Pages: 20 (2022)
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| 42 |
Sign Language Avatars: A Question of Representation
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In: Information; Volume 13; Issue 4; Pages: 206 (2022)
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| 43 |
Identifying Source-Language Dialects in Translation
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In: Mathematics; Volume 10; Issue 9; Pages: 1431 (2022)
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| 44 |
Leveraging Frozen Pretrained Written Language Models for Neural Sign Language Translation
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In: Information; Volume 13; Issue 5; Pages: 220 (2022)
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| 45 |
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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Abstract:
Machine translation has received significant attention in the field of natural language processing not only because of its challenges but also due to the translation needs that arise in the daily life of modern people. In this study, we design a new machine translation model named X-Transformer, which refines the original Transformer model regarding three aspects. First, the model parameter of the encoder is compressed. Second, the encoder structure is modified by adopting two layers of the self-attention mechanism consecutively and reducing the point-wise feed forward layer to help the model understand the semantic structure of sentences precisely. Third, we streamline the decoder model size, while maintaining the accuracy. Through experiments, we demonstrate that having a large number of decoder layers not only affects the performance of the translation model but also increases the inference time. The X-Transformer reaches the state-of-the-art result of 46.63 and 55.63 points in the BiLingual Evaluation Understudy (BLEU) metric of the World Machine Translation (WMT), from 2014, using the English–German and English–French translation corpora, thus outperforming the Transformer model with 19 and 18 BLEU points, respectively. The X-Transformer significantly reduces the training time to only 1/3 times that of the Transformer. In addition, the heat maps of the X-Transformer reach token-level precision (i.e., token-to-token attention), while the Transformer model remains at the sentence level (i.e., token-to-sentence attention).
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Keyword:
machine translation; natural language processing
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URL: https://doi.org/10.3390/app12094502
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| 46 |
Retrieval-Based Transformer Pseudocode Generation
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In: Mathematics; Volume 10; Issue 4; Pages: 604 (2022)
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| 47 |
Evaluating the Impact of Integrating Similar Translations into Neural Machine Translation
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In: Information; Volume 13; Issue 1; Pages: 19 (2022)
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| 48 |
Hebrew Transformed: Machine Translation of Hebrew Using the Transformer Architecture
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| 49 |
Technology in audiovisual translation practices and training ; Las tecnologías en la formación y las prácticas de traducción audiovisual
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In: CLINA Revista Interdisciplinaria de Traducción Interpretación y Comunicación Intercultural; Vol. 7 Núm. 1 (2021); 17-24 ; CLINA Revista Interdisciplinaria de Traducción Interpretación y Comunicación Intercultural; Vol. 7 No. 1 (2021); 17-24 ; 2444-1961 ; 10.14201/clina202171 (2022)
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| 50 |
Training in machine translation post-editing for foreign language students
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Zhang, Hong; Torres-Hostench, Olga. - : University of Hawaii National Foreign Language Resource Center, 2022. : Center for Language & Technology, 2022. : (co-sponsored by Center for Open Educational Resources and Language Learning, University of Texas at Austin), 2022
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| 51 |
Pushing the right buttons: adversarial evaluation of quality estimation
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In: Proceedings of the Sixth Conference on Machine Translation ; 625 ; 638 (2022)
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| 52 |
Some Contributions to Interactive Machine Translation and to the Applications of Machine Translation for Historical Documents
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| 53 |
Neural-based Knowledge Transfer in Natural Language Processing
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| 54 |
Machine Translation of polysemic words: current technology in light of Cognitive Linguistics ; Tradução automática de palavras polissêmicas: tecnologias atuais à luz da Linguística Cognitiva
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In: Entrepalavras; v. 11, n. 3 (11): Linguagem e Tecnologia; 52-74 (2022)
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| 55 |
The role of machine translation in translation education: A thematic analysis of translator educators’ beliefs
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In: Translation and Interpreting : the International Journal of Translation and Interpreting Research, Vol 14, Iss 1, Pp 177-197 (2022) (2022)
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| 56 |
Predicting and Critiquing Machine Virtuosity: Mawwal Accompaniment as Case Study
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In: International Computer Music Conference ; https://hal.archives-ouvertes.fr/hal-03044066 ; International Computer Music Conference, Jul 2021, Santiago, Chile (2021)
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| 57 |
Investigating alignment interpretability for low-resource NMT
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In: ISSN: 0922-6567 ; EISSN: 1573-0573 ; Machine Translation ; https://hal.archives-ouvertes.fr/hal-03139744 ; Machine Translation, Springer Verlag, 2021, ⟨10.1007/s10590-020-09254-w⟩ (2021)
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| 58 |
Parallel Corpora Preparation for English-Amharic Machine Translation
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In: IWANN 2021 - International Work on Artificial Neural Networks, Conference Springer LNCS proceedings ; https://hal.inria.fr/hal-03272258 ; IWANN 2021 - International Work on Artificial Neural Networks, Conference Springer LNCS proceedings, Jun 2021, Online, Spain (2021)
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| 59 |
DESIGNING TO SUPPORT SENSEMAKING IN CROSS-LINGUAL COMPUTER-MEDIATED COMMUNICATION USING NLP TECHNIQUES
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| 60 |
DELA Corpus - A Document-Level Corpus Annotated with Context-Related Issues
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In: Castilho, Sheila orcid:0000-0002-8416-6555 , Cavalheiro Camargo, João Lucas orcid:0000-0003-3746-1225 , Menezes, Miguel and Way, Andy orcid:0000-0001-5736-5930 (2021) DELA Corpus - A Document-Level Corpus Annotated with Context-Related Issues. In: Sixth Conference on Machine Translation (WMT21), 10-11 Nov 2021, Punta Cana, Dominican Republic (Online). ISBN 978-1-954085-94-7 (2021)
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