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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 101–150 of 1773 papers

TitleStatusHype
Pointer-Generator Networks for Low-Resource Machine Translation: Don't Copy That!Code0
To Label or Not to Label: Hybrid Active Learning for Neural Machine Translation—0
BiVert: Bidirectional Vocabulary Evaluation using Relations for Machine Translation—0
General2Specialized LLMs Translation for E-commerce—0
Human Evaluation of English--Irish Transformer-Based NMT—0
Leveraging Diverse Modeling Contexts with Collaborating Learning for Neural Machine Translation—0
DeMPT: Decoding-enhanced Multi-phase Prompt Tuning for Making LLMs Be Better Context-aware TranslatorsCode0
GATE X-E : A Challenge Set for Gender-Fair Translations from Weakly-Gendered Languages—0
Asynchronous and Segmented Bidirectional Encoding for NMT—0
Quality Does Matter: A Detailed Look at the Quality and Utility of Web-Mined Parallel CorporaCode0
Large Language Models "Ad Referendum": How Good Are They at Machine Translation in the Legal Domain?—0
Promoting Target Data in Context-aware Neural Machine Translation—0
Neural Machine Translation for Malayalam Paraphrase Generation—0
Salute the Classic: Revisiting Challenges of Machine Translation in the Age of Large Language ModelsCode0
Only Send What You Need: Learning to Communicate Efficiently in Federated Multilingual Machine Translation—0
An approach for mistranslation removal from popular dataset for Indic MT Task—0
Machine Translation Models are Zero-Shot Detectors of Translation DirectionCode0
End to end Hindi to English speech conversion using Bark, mBART and a finetuned XLSR Wav2Vec2—0
Towards Boosting Many-to-Many Multilingual Machine Translation with Large Language ModelsCode0
POMP: Probability-driven Meta-graph Prompter for LLMs in Low-resource Unsupervised Neural Machine Translation—0
Convergences and Divergences between Automatic Assessment and Human Evaluation: Insights from Comparing ChatGPT-Generated Translation and Neural Machine Translation—0
Predicting Human Translation Difficulty with Neural Machine Translation—0
An Empirical study of Unsupervised Neural Machine Translation: analyzing NMT output, model's behavior and sentences' contribution—0
Distinguishing Translations by Human, NMT, and ChatGPT: A Linguistic and Statistical Approach—0
Unraveling Key Factors of Knowledge Distillation—0
Order Matters in the Presence of Dataset Imbalance for Multilingual Learning—0
Improving Neural Machine Translation by Multi-Knowledge Integration with Prompting—0
Simul-LLM: A Framework for Exploring High-Quality Simultaneous Translation with Large Language Models—0
Relevance-guided Neural Machine Translation—0
Reducing Gender Bias in Machine Translation through Counterfactual Data Generation—0
DP-NMT: Scalable Differentially-Private Machine TranslationCode0
Context-aware Neural Machine Translation for English-Japanese Business Scene DialoguesCode0
On Using Distribution-Based Compositionality Assessment to Evaluate Compositional Generalisation in Machine TranslationCode0
On-the-Fly Fusion of Large Language Models and Machine Translation—0
Direct Preference Optimization for Neural Machine Translation with Minimum Bayes Risk DecodingCode1
Don't Overlook the Grammatical Gender: Bias Evaluation for Hindi-English Machine TranslationCode0
There's no Data Like Better Data: Using QE Metrics for MT Data Filtering—0
Memorisation Cartography: Mapping out the Memorisation-Generalisation Continuum in Neural Machine Translation—0
Gender Inflected or Bias Inflicted: On Using Grammatical Gender Cues for Bias Evaluation in Machine TranslationCode0
Improving Machine Translation with Large Language Models: A Preliminary Study with Cooperative DecodingCode0
Replicable Benchmarking of Neural Machine Translation (NMT) on Low-Resource Local Languages in IndonesiaCode0
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
knn-seq: Efficient, Extensible kNN-MT FrameworkCode1
Enhancing Neural Machine Translation with Semantic UnitsCode0
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