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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 801–850 of 1773 papers

TitleStatusHype
Self-Learning for Zero Shot Neural Machine Translation—0
Translating the Unseen? Yoruba-English MT in Low-Resource, Morphologically-Unmarked SettingsCode0
Multi-Domain Adaptation in Neural Machine Translation Through Multidimensional TaggingCode0
Meta Back-translation—0
Improving Zero-shot Neural Machine Translation on Language-specific Encoders-Decoders—0
Towards Personalised and Document-level Machine Translation of Dialogue—0
Understanding Pre-Editing for Black-Box Neural Machine Translation—0
Machine Translationese: Effects of Algorithmic Bias on Linguistic Complexity in Machine Translation—0
Synthesizing Monolingual Data for Neural Machine Translation—0
Efficient Neural Machine Translation with Prior Word Alignment—0
Crowd-sourced Phrase-Based Tokenization for Low-Resourced Neural Machine Translation: The case of Fon Language—0
Learning to Use Future Information in Simultaneous TranslationCode0
Translation Memory Guided Neural Machine Translation—0
Switching-Aligned-Words Data Augmentation for Neural Machine Translation—0
A Simple and General Strategy for Referential Problem in Low-Resource Neural Machine Translation—0
Neural Machine Translation: A Review of Methods, Resources, and Tools—0
Revisiting Robust Neural Machine Translation: A Transformer Case Study—0
FGraDA: A Dataset and Benchmark for Fine-Grained Domain Adaptation in Machine TranslationCode0
Synthetic Source Language Augmentation for Colloquial Neural Machine Translation—0
Why Neural Machine Translation Prefers Empty Outputs—0
Domain Adaptation of NMT models for English-Hindi Machine Translation Task at AdapMT ICON 2020—0
Modeling Homophone Noise for Robust Neural Machine Translation—0
Approches quantitatives de l'analyse des prédictions en traduction automatique neuronale (TAN)—0
Rewriter-Evaluator Architecture for Neural Machine Translation—0
Exploring Pair-Wise NMT for Indian Languages—0
Document Graph for Neural Machine Translation—0
Reciprocal Supervised Learning Improves Neural Machine TranslationCode0
Efforts Towards Developing a Tamang Nepali Machine Translation System—0
Effective Use of Target-side Context for Neural Machine Translation—0
Bridging Philippine Languages With Multilingual Neural Machine Translation—0
Domain Adaptation of NMT models for English-Hindi Machine Translation Task : AdapMT Shared Task ICON 2020—0
A Document-Level Neural Machine Translation Model with Dynamic Caching Guided by Theme-Rheme InformationCode0
An Error-based Investigation of Statistical and Neural Machine Translation Performance on Hindi-to-Tamil and English-to-Tamil—0
Terminology-Aware Sentence Mining for NMT Domain Adaptation: ADAPT’s Submission to the Adap-MT 2020 English-to-Hindi AI Translation Shared Task—0
Meta Ensemble for Japanese-Chinese Neural Machine Translation: Kyoto-U+ECNU Participation to WAT 2020—0
Korean-to-Japanese Neural Machine Translation System using Hanja Information—0
Knowledge Graph Enhanced Neural Machine Translation via Multi-task Learning on Sub-entity Granularity—0
Machine-oriented NMT Adaptation for Zero-shot NLP tasks: Comparing the Usefulness of Close and Distant Languages—0
PhraseOut: A Code Mixed Data Augmentation Method for MultilingualNeural Machine Tranlsation—0
Neural Machine Translation Doesn’t Translate Gender Coreference Right Unless You Make ItCode0
Investigating Low-resource Machine Translation for English-to-Tamil—0
BERT Enhanced Neural Machine Translation and Sequence Tagging Model for Chinese Grammatical Error Diagnosis—0
NICT‘s Submission To WAT 2020: How Effective Are Simple Many-To-Many Neural Machine Translation Models?—0
An Effective Optimization Method for Neural Machine Translation: The Case of English-Persian Bilingually Low-Resource Scenario—0
NLPRL Odia-English: Indic Language Neural Machine Translation System—0
Improving NMT via Filtered Back Translation—0
Neural Machine Translation for translating into Croatian and Serbian—0
Neural Machine Translation Models with Back-Translation for the Extremely Low-Resource Indigenous Language BribriCode0
Improving Low-Resource NMT through Relevance Based Linguistic Features Incorporation—0
Identifying Complaints from Product Reviews: A Case Study on HindiCode0
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