Language modeling and machine translation: improvements in training and modeling
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
Online-Ressource
- Language
-
Englisch
- Notes
-
RWTH Aachen University, Dissertation, 2024
- Keyword
-
Maschinelle Übersetzung
- Event
-
Veröffentlichung
- (where)
-
Aachen
- (who)
-
Universitätsbibliothek der RWTH Aachen
- (when)
-
2024
- Creator
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Yingbo, Gao
- Contributor
- DOI
-
10.18154/RWTH-2024-08232
- URN
-
urn:nbn:de:101:1-2410110143510.674800349749
- Rights
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
25.03.2025, 1:50 PM CET
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Yingbo, Gao
- Ney, Hermann
- Yvon, Francois
- Universitätsbibliothek der RWTH Aachen
Time of origin
- 2024
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Training and models for statistical machine translation
Effective training and efficient decoding for statistical machine translation
Handling multimodality and scarce resources in sign language machine translation
Improvements in language and translation modeling
Neural sequence-to-sequence modeling for language and speech translation
Neural network based modeling and architectures for automatic speech recognition and machine translation
Two-Way Neural Machine Translation : A Proof of Concept for Bidirectional Translation Modeling using a Two-Dimensional Grid
Data-driven deep modeling and training for automatic speech recognition
Search and training with joint translation and reordering models for statistical machine translation
Discriminative feature modeling for statistical speech recognition
Advancing neural language modeling in automatic speech recognition
Investigation into Joint Optimization of Single Channel Speech Enhancement and Acoustic Modeling for Robust ASR
Training and models for statistical machine translation
Effective training and efficient decoding for statistical machine translation