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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 1176–1200 of 1773 papers

TitleStatusHype
Low-Resource Machine Translation Training Curriculum Fit for Low-Resource Languages—0
Using Interlinear Glosses as Pivot in Low-Resource Multilingual Machine Translation—0
Low Resource Multimodal Neural Machine Translation of English-Hindi in News Domain—0
Low-resource Neural Machine Translation: Benchmarking State-of-the-art Transformer for Wolof<->French—0
LTRC-MT Simple \& Effective Hindi-English Neural Machine Translation Systems at WAT 2019—0
Machine-oriented NMT Adaptation for Zero-shot NLP tasks: Comparing the Usefulness of Close and Distant Languages—0
Machine Translation at Booking.com: Journey and Lessons Learned—0
Machine Translation by Projecting Text into the Same Phonetic-Orthographic Space Using a Common Encoding—0
Machine Translationese: Effects of Algorithmic Bias on Linguistic Complexity in Machine Translation—0
Machine Translation for English–Inuktitut with Segmentation, Data Acquisition and Pre-Training—0
Machine Translation for Livonian: Catering to 20 Speakers—0
Machine Translation for Machines: the Sentiment Classification Use Case—0
Machine Translation of 16Th Century Letters from Latin to German—0
Machine Translation Quality: A comparative evaluation of SMT, NMT and tailored-NMT outputs—0
MacNet: Transferring Knowledge from Machine Comprehension to Sequence-to-Sequence Models—0
Make the Blind Translator See The World: A Novel Transfer Learning Solution for Multimodal Machine Translation—0
Massively Multilingual Neural Machine Translation—0
Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges—0
Master Thesis: Neural Sign Language Translation by Learning Tokenization—0
KÚ <MASK>: Integrating Yorùbá cultural greetings into machine translation—0
MBR and QE Finetuning: Training-time Distillation of the Best and Most Expensive Decoding Methods—0
Meaningless yet meaningful: Morphology grounded subword-level NMT—0
Measuring and Improving Faithfulness of Attention in Neural Machine Translation—0
Measuring and Mitigating Name Biases in Neural Machine Translation—0
Meet Changes with Constancy: Learning Invariance in Multi-Source Translation—0
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