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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 1651–1700 of 1773 papers

TitleStatusHype
Neural Machine Translation Models with Attention-Based Dropout LayerCode0
Neural Machine Translation Models with Back-Translation for the Extremely Low-Resource Indigenous Language BribriCode0
Energy-Based Reranking: Improving Neural Machine Translation Using Energy-Based ModelsCode0
Vocabulary Adaptation for Domain Adaptation in Neural Machine TranslationCode0
Sinhala Transliteration: A Comparative Analysis Between Rule-based and Seq2Seq ApproachesCode0
Quality Does Matter: A Detailed Look at the Quality and Utility of Web-Mined Parallel CorporaCode0
End-to-End Training for Back-Translation with Categorical Reparameterization TrickCode0
Dense Information Flow for Neural Machine TranslationCode0
Joey NMT: A Minimalist NMT Toolkit for NovicesCode0
Encoder-Decoder Shift-Reduce Syntactic ParsingCode0
Joint Dropout: Improving Generalizability in Low-Resource Neural Machine Translation through Phrase Pair VariablesCode0
Neural Machine Translation of Text from Non-Native SpeakersCode0
Towards Neural Phrase-based Machine TranslationCode0
Neural Machine Translation Quality and Post-Editing PerformanceCode0
DeMPT: Decoding-enhanced Multi-phase Prompt Tuning for Making LLMs Be Better Context-aware TranslatorsCode0
Neural Machine Translation Techniques for Named Entity TransliterationCode0
Neural Machine Translation with ReconstructionCode0
Adapting Multilingual Neural Machine Translation to Unseen LanguagesCode0
Benchmarking Machine Translation with Cultural AwarenessCode0
A Latent Morphology Model for Open-Vocabulary Neural Machine TranslationCode0
Deep Recurrent Models with Fast-Forward Connections for Neural Machine TranslationCode0
Active Learning for Neural Machine TranslationCode0
Neural Machine Translation with Error CorrectionCode0
Translation Transformers Rediscover Inherent Data DomainsCode0
Neural Machine Translation with Heterogeneous Topic Knowledge EmbeddingsCode0
Finding the Optimal Vocabulary Size for Neural Machine TranslationCode0
Kyoto-NMT: a Neural Machine Translation implementation in ChainerCode0
Deep Neural Machine Translation with Weakly-Recurrent UnitsCode0
Efficient k-Nearest-Neighbor Machine Translation with Dynamic RetrievalCode0
Are We Paying Attention to Her? Investigating Gender Disambiguation and Attention in Machine TranslationCode0
Deconvolution-Based Global Decoding for Neural Machine TranslationCode0
Efficient Cluster-Based k-Nearest-Neighbor Machine TranslationCode0
Deceiving Question-Answering Models: A Hybrid Word-Level Adversarial ApproachCode0
Reciprocal Supervised Learning Improves Neural Machine TranslationCode0
A Retrieve-and-Rewrite Initialization Method for Unsupervised Machine TranslationCode0
Recurrent Attention for Neural Machine TranslationCode0
Debugging Neural Machine TranslationsCode0
A Copy Mechanism for Handling Knowledge Base Elements in SPARQL Neural Machine TranslationCode0
Wino-X: Multilingual Winograd Schemas for Commonsense Reasoning and Coreference ResolutionCode0
Neural Sign Language TranslationCode0
AFRIDOC-MT: Document-level MT Corpus for African LanguagesCode0
Cultural and Geographical Influences on Image Translatability of Words across LanguagesCode0
The paradox of the compositionality of natural language: a neural machine translation case studyCode0
RedPenNet for Grammatical Error Correction: Outputs to Tokens, Attentions to SpansCode0
Sockeye: A Toolkit for Neural Machine TranslationCode0
Effective Cross-lingual Transfer of Neural Machine Translation Models without Shared VocabulariesCode0
Tree-to-Sequence Attentional Neural Machine TranslationCode0
Ngambay-French Neural Machine Translation (sba-Fr)Code0
Towards Robust Neural Machine Translation with Iterative Scheduled Data-Switch TrainingCode0
Character-level Chinese-English Translation through ASCII EncodingCode0
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