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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 851–875 of 1773 papers

TitleStatusHype
Improving Low-Resource NMT through Relevance Based Linguistic Features Incorporation—0
Identifying Complaints from Product Reviews: A Case Study on HindiCode0
Neural Machine Translation Models with Back-Translation for the Extremely Low-Resource Indigenous Language BribriCode0
ContraCAT: Contrastive Coreference Analytical Templates for Machine Translation—0
NICT‘s Submission To WAT 2020: How Effective Are Simple Many-To-Many Neural Machine Translation Models?—0
Heterogeneous Recycle Generation for Chinese Grammatical Error Correction—0
Neural Machine Translation Doesn’t Translate Gender Coreference Right Unless You Make ItCode0
Terminology-Aware Sentence Mining for NMT Domain Adaptation: ADAPT’s Submission to the Adap-MT 2020 English-to-Hindi AI Translation Shared Task—0
A Test Suite for Evaluating Discourse Phenomena in Document-level Neural Machine Translation—0
Zero-shot translation among Indian languages—0
NLPRL Odia-English: Indic Language Neural Machine Translation System—0
Comparison of the effects of attention mechanism on translation tasks of different lengths of ambiguous words—0
Filtering Back-Translated Data in Unsupervised Neural Machine Translation—0
Multilingual Neural Machine Translation—0
Machine-oriented NMT Adaptation for Zero-shot NLP tasks: Comparing the Usefulness of Close and Distant Languages—0
A Review of Discourse-level Machine Translation—0
AdapNMT : Neural Machine Translation with Technical Domain Adaptation for Indic Languages—0
English-to-Chinese Transliteration with Phonetic Auxiliary Task—0
MUCS@Adap-MT 2020: Low Resource Domain Adaptation for Indic Machine Translation—0
Dynamic Curriculum Learning for Low-Resource Neural Machine Translation—0
Machine Translation of Novels in the Age of TransformerCode0
Decoding and Diversity in Machine Translation—0
Master Thesis: Neural Sign Language Translation by Learning Tokenization—0
A Hybrid Approach for Improved Low Resource Neural Machine Translation using Monolingual Data—0
Inference-only sub-character decomposition improves translation of unseen logographic characters—0
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