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Overview and challenges of machine translation for contextually appropriate translations

Naveen, Palanichamy, Trojovský, Pavel·2024·iScience
DOI 10.1016/j.isci.2024.110878PMC PMC11465115
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Graphical abstract CC BY · PubMed Central
Figure 1 Types of machine translation
Figure 1 Types of machine translationCC BY · PubMed Central
Figure 2 Architecture of neural machine translation (NMT) Models, highlighting the Encoder-Decoder structure and the Attention Mechanism The encoder processes the input sequence and generates context vectors, which are used by the attention mechanism to help the decoder generate the translated output.
Figure 2 Architecture of neural machine translation (NMT) Models, highlighting the Encoder-Decoder structure and the Attention Mechanism The encoder processes the input sequence and generates context vectors, which are used by the attention mechanism to help the decoder generate the translated output.CC BY · PubMed Central
Figure 3 The rise of NMT models
Figure 3 The rise of NMT modelsCC BY · PubMed Central
Figure 4 Key metrics of machine translation
Figure 4 Key metrics of machine translationCC BY · PubMed Central
Figure 5 A few machine translation applications
Figure 5 A few machine translation applicationsCC BY · PubMed Central

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Metadata

Authors
Naveen, P., & Trojovský, P.
Venue
iScience
Publisher
Elsevier BV
Published
2024-10
Volume
27
Issue
10
Pages
110878
ISSN
2589-0042
DOI
10.1016/j.isci.2024.110878
PMC
PMC11465115
Cited by
103
References
0
Language
en
Licence
Funders
Univerzita Hradec Králové (2226/2024)

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