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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 1301–1350 of 1773 papers

TitleStatusHype
Neural Machine Translation for Code Generation—0
Neural Machine Translation for Low Resource Languages using Bilingual Lexicon Induced from Comparable Corpora—0
Neural machine translation for low-resource languages—0
Neural Machine Translation for Low-Resource Languages: A Survey—0
Neural Machine Translation for Malayalam Paraphrase Generation—0
Neural Machine Translation for Sinhala-English Code-Mixed Text—0
Neural Machine Translation for South Africa's Official Languages—0
Neural Machine Translation for translating into Croatian and Serbian—0
NMT-based Cross-lingual Document Embeddings—0
Neural Machine Translation from Simplified Translations—0
Neural Machine Translation: Hindi-Nepali—0
Neural Machine Translation Incorporating Named Entity—0
Neural Machine Translation in Low-Resource Setting: a Case Study in English-Marathi Pair—0
Neural Machine Translation Leveraging Phrase-based Models in a Hybrid Search—0
Neural Machine Translation model for University Email Application—0
Neural Machine Translation Model with a Large Vocabulary Selected by Branching Entropy—0
Neural Machine Translation of Logographic Languages Using Sub-character Level Information—0
Neural Machine Translation of Logographic Language Using Sub-character Level Information—0
Neural Machine Translation on Scarce-Resource Condition: A case-study on Persian-English—0
Neural Machine Translation with 4-Bit Precision and Beyond—0
Neural Machine Translation with Adequacy-Oriented Learning—0
Neural Machine Translation with Decoding History Enhanced Attention—0
Neural Machine Translation with Explicit Phrase Alignment—0
Neural Machine Translation with Key-Value Memory-Augmented Attention—0
Neural Machine Translation with Source Dependency Representation—0
Neural Machine Translation with Source-Side Latent Graph Parsing—0
Neural Machine Translation with Supervised Attention—0
Neural Machine Translation with Word Predictions—0
Neural Name Translation Improves Neural Machine Translation—0
Neural Simultaneous Speech Translation Using Alignment-Based Chunking—0
Neural Speech Translation at AppTek—0
Neural System Combination for Machine Translation—0
Neural versus Phrase-Based Machine Translation Quality: a Case Study—0
Neural vs. Phrase-Based Machine Translation in a Multi-Domain Scenario—0
N-gram Prediction and Word Difference Representations for Language Modeling—0
NHK’s Lexically-Constrained Neural Machine Translation at WAT 2021—0
NICT-5’s Submission To WAT 2021: MBART Pre-training And In-Domain Fine Tuning For Indic Languages—0
NICT's Corpus Filtering Systems for the WMT18 Parallel Corpus Filtering Task—0
NICT Self-Training Approach to Neural Machine Translation at NMT-2018—0
NICT's Machine Translation Systems for the WMT19 Similar Language Translation Task—0
NICT's Neural and Statistical Machine Translation Systems for the WMT18 News Translation Task—0
NICT's participation to WAT 2019: Multilingualism and Multi-step Fine-Tuning for Low Resource NMT—0
NICT’s Submission to the WAT 2022 Structured Document Translation Task—0
NICT‘s Submission To WAT 2020: How Effective Are Simple Many-To-Many Neural Machine Translation Models?—0
NICT's Supervised Neural Machine Translation Systems for the WMT19 News Translation Task—0
NICT's Supervised Neural Machine Translation Systems for the WMT19 Translation Robustness Task—0
NITK-UoH: Tamil-Telugu Machine Translation Systems for the WMT21 Similar Language Translation Task—0
NIT Rourkela Machine Translation(MT) System Submission to WAT 2022 for MultiIndicMT: An Indic Language Multilingual Shared Task—0
NLPRL Odia-English: Indic Language Neural Machine Translation System—0
NMT and PBSMT Error Analyses in English to Brazilian Portuguese Automatic Translations—0
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