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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 1476–1500 of 1773 papers

TitleStatusHype
A Document-Level Neural Machine Translation Model with Dynamic Caching Guided by Theme-Rheme InformationCode0
Generating Authentic Adversarial Examples beyond Meaning-preserving with Doubly Round-trip TranslationCode0
TransFool: An Adversarial Attack against Neural Machine Translation ModelsCode0
A Comparative Study of LLMs, NMT Models, and Their Combination in Persian-English Idiom TranslationCode0
Exploring Unsupervised Pretraining Objectives for Machine TranslationCode0
Token Drop mechanism for Neural Machine TranslationCode0
Exploring Recombination for Efficient Decoding of Neural Machine TranslationCode0
Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot TranslationCode0
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine TranslationCode0
Granularity is crucial when applying differential privacy to text: An investigation for neural machine translationCode0
Rule-based Morphological Inflection Improves Neural Terminology TranslationCode0
Modeling Past and Future for Neural Machine TranslationCode0
Greedy Search with Probabilistic N-gram Matching for Neural Machine TranslationCode0
Guided Alignment Training for Topic-Aware Neural Machine TranslationCode0
Saliency-driven Word Alignment Interpretation for Neural Machine TranslationCode0
Guiding attention in Sequence-to-sequence models for Dialogue Act predictionCode0
Incorporating Chinese Radicals Into Neural Machine Translation: Deeper Than Character LevelCode0
Salute the Classic: Revisiting Challenges of Machine Translation in the Age of Large Language ModelsCode0
Byte-based Multilingual NMT for Endangered LanguagesCode0
Domain Robustness in Neural Machine TranslationCode0
Handling Syntactic Divergence in Low-resource Machine TranslationCode0
Bridging the Gap between Training and Inference: Multi-Candidate Optimization for Diverse Neural Machine TranslationCode0
Harnessing Cross-lingual Features to Improve Cognate Detection for Low-resource LanguagesCode0
On Using Distribution-Based Compositionality Assessment to Evaluate Compositional Generalisation in Machine TranslationCode0
Exploring Paracrawl for Document-level Neural Machine TranslationCode0
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