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NMT

Neural machine translation is an approach to machine translation that uses an artificial neural network to predict the likelihood of a sequence of words, typically modeling entire sentences in a single integrated model.

Papers

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Showing 1526–1550 of 1773 papers

TitleStatusHype
CREAMT: Creativity and narrative engagement of literary texts translated by translators and NMT—0
Cross-Corpora Evaluation and Analysis of Grammatical Error Correction Models --- Is Single-Corpus Evaluation Enough?—0
Cross-lingual Human-Preference Alignment for Neural Machine Translation with Direct Quality Optimization—0
Cross-lingual Pre-training Based Transfer for Zero-shot Neural Machine Translation—0
Cross-Lingual Transformers for Neural Automatic Post-Editing—0
Crowd-sourced Phrase-Based Tokenization for Low-Resourced Neural Machine Translation: The case of Fon Language—0
Crowdsourced Phrase-Based Tokenization for Low-Resourced Neural Machine Translation: The Case of Fon Language—0
CSP:Code-Switching Pre-training for Neural Machine Translation—0
CUNI English-Czech and English-Polish Systems in WMT20: Robust Document-Level Training—0
CUNI NMT System for WAT 2017 Translation Tasks—0
CUNI NMT System for WAT 2018 Translation Tasks—0
CUNI System for the Building Educational Applications 2019 Shared Task: Grammatical Error Correction—0
CUNI Systems in WMT21: Revisiting Backtranslation Techniques for English-Czech NMT—0
CUNI Transformer Neural MT System for WMT18—0
Current Shortcomings of Machine Translation in Spanish and Bulgarian Vis-à-vis English—0
Curriculum Learning and Minibatch Bucketing in Neural Machine Translation—0
Cyber Risks of Machine Translation Critical Errors : Arabic Mental Health Tweets as a Case Study—0
DaLC: Domain Adaptation Learning Curve Prediction for Neural Machine Translation—0
Data Augmentation for Sign Language Gloss Translation—0
Data Augmentation to Address Out-of-Vocabulary Problem in Low-Resource Sinhala-English Neural Machine Translation—0
Data augmentation using back-translation for context-aware neural machine translation—0
Data Augmentation With Back translation for Low Resource languages: A case of English and Luganda—0
Data Scaling Laws in NMT: The Effect of Noise and Architecture—0
Data Selection Curriculum for Neural Machine Translation—0
Data Selection with Feature Decay Algorithms Using an Approximated Target Side—0
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