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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 301–350 of 1773 papers

TitleStatusHype
Integrating Pre-trained Language Model into Neural Machine Translation—0
Reference Free Domain Adaptation for Translation of Noisy Questions with Question Specific RewardsCode0
Contextual Refinement of Translations: Large Language Models for Sentence and Document-Level Post-Editing—0
Code-Switching with Word Senses for Pretraining in Neural Machine Translation—0
On Synthetic Data for Back TranslationCode0
Ask Language Model to Clean Your Noisy Translation Data—0
Direct Neural Machine Translation with Task-level Mixture of Experts models—0
Enhancing Neural Machine Translation with Semantic UnitsCode0
Towards a Better Understanding of Variations in Zero-Shot Neural Machine Translation PerformanceCode0
Towards Example-Based NMT with Multi-Levenshtein TransformersCode0
Quality-Aware Translation Models: Efficient Generation and Quality Estimation in a Single Model—0
Hindi to English: Transformer-Based Neural Machine Translation—0
Domain Adaptation for Arabic Machine Translation: The Case of Financial Texts—0
OSN-MDAD: Machine Translation Dataset for Arabic Multi-Dialectal Conversations on Online Social Media—0
Towards Effective Disambiguation for Machine Translation with Large Language Models—0
RedPenNet for Grammatical Error Correction: Outputs to Tokens, Attentions to SpansCode0
MBR and QE Finetuning: Training-time Distillation of the Best and Most Expensive Decoding Methods—0
Epi-Curriculum: Episodic Curriculum Learning for Low-Resource Domain Adaptation in Neural Machine Translation—0
Gender-specific Machine Translation with Large Language Models—0
Impact of Visual Context on Noisy Multimodal NMT: An Empirical Study for English to Indian LanguagesCode0
A Classification-Guided Approach for Adversarial Attacks against Neural Machine TranslationCode0
An Empirical Study of Consistency Regularization for End-to-End Speech-to-Text TranslationCode0
Ngambay-French Neural Machine Translation (sba-Fr)Code0
An Effective Method using Phrase Mechanism in Neural Machine TranslationCode0
Is context all you need? Scaling Neural Sign Language Translation to Large Domains of Discourse—0
Fast Training of NMT Model with Data Sorting—0
Evaluating and Optimizing the Effectiveness of Neural Machine Translation in Supporting Code Retrieval Models: A Study on the CAT Benchmark—0
Character-level NMT and language similarity—0
SelfSeg: A Self-supervised Sub-word Segmentation Method for Neural Machine Translation—0
Joint Dropout: Improving Generalizability in Low-Resource Neural Machine Translation through Phrase Pair VariablesCode0
Improving End-to-End Speech Translation by Imitation-Based Knowledge Distillation with Synthetic TranscriptsCode0
Enhancing Supervised Learning with Contrastive Markings in Neural Machine Translation Training—0
Syntax-Aware Complex-Valued Neural Machine Translation—0
Pluggable Neural Machine Translation Models via Memory-augmented AdaptersCode0
xSIM++: An Improved Proxy to Bitext Mining Performance for Low-Resource Languages—0
Towards Accurate Translation via Semantically Appropriate Application of Lexical ConstraintsCode0
A Relaxed Optimization Approach for Adversarial Attacks against Neural Machine Translation Models—0
Learning Multilingual Sentence Representations with Cross-lingual Consistency RegularizationCode0
Textual Augmentation Techniques Applied to Low Resource Machine Translation: Case of Swahili—0
Rethinking Translation Memory Augmented Neural Machine Translation—0
On Search Strategies for Document-Level Neural Machine Translation—0
Improving Long Context Document-Level Machine Translation—0
Extract and Attend: Improving Entity Translation in Neural Machine Translation—0
Leveraging Auxiliary Domain Parallel Data in Intermediate Task Fine-tuning for Low-resource TranslationCode0
Assessing the Importance of Frequency versus Compositionality for Subword-based Tokenization in NMTCode0
How Does Pretraining Improve Discourse-Aware Translation?—0
Translation-Enhanced Multilingual Text-to-Image Generation—0
Augmenting Large Language Model Translators via Translation Memories—0
Do GPTs Produce Less Literal Translations?Code0
On the Copying Problem of Unsupervised NMT: A Training Schedule with a Language Discriminator LossCode0
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