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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 1501–1550 of 1773 papers

TitleStatusHype
Context-Aware Smoothing for Neural Machine Translation—0
Context Based Machine Translation With Recurrent Neural Network For English-Amharic Translation—0
Context in Neural Machine Translation: A Review of Models and Evaluations—0
Contextual Cues in Machine Translation: Investigating the Potential of Multi-Source Input Strategies in LLMs and NMT Systems—0
Contextual Refinement of Translations: Large Language Models for Sentence and Document-Level Post-Editing—0
Contextual Text Embeddings for Twi—0
Continual Learning for Neural Machine Translation—0
Continual Learning in Multilingual NMT via Language-Specific Embeddings—0
ContraCAT: Contrastive Coreference Analytical Templates for Machine Translation—0
Contrastive Learning for Context-aware Neural Machine TranslationUsing Coreference Information—0
Contrastive Learning for Context-aware Neural Machine Translation Using Coreference Information—0
Contrastive Token Learning with Similarity Decay for Repetition Suppression in Machine Translation—0
Contrastive Word Embedding Learning for Neural Machine Translation—0
Controlling Formality in Low-Resource NMT with Domain Adaptation and Re-Ranking: SLT-CDT-UoS at IWSLT2022—0
Controlling Japanese Honorifics in English-to-Japanese Neural Machine Translation—0
Controlling the Output Length of Neural Machine Translation—0
Controlling Utterance Length in NMT-based Word Segmentation with Attention—0
Coreference and Coherence in Neural Machine Translation: A Study Using Oracle Experiments—0
Corpora for Document-Level Neural Machine Translation—0
Corpus Augmentation by Sentence Segmentation for Low-Resource Neural Machine Translation—0
Correcting Length Bias in Neural Machine Translation—0
Cost-Effective Training in Low-Resource Neural Machine Translation—0
Cost-Effective Training in Low-Resource Neural Machine Translation—0
Coverage Embedding Models for Neural Machine Translation—0
Crafting Adversarial Examples for Neural Machine Translation—0
CREAMT: Creativity and narrative engagement of literary texts translated by translators and NMT—0
Cross-Corpora Evaluation and Analysis of Grammatical Error Correction Models --- Is Single-Corpus Evaluation Enough?—0
Cross-lingual Human-Preference Alignment for Neural Machine Translation with Direct Quality Optimization—0
Cross-lingual Pre-training Based Transfer for Zero-shot Neural Machine Translation—0
Cross-Lingual Transformers for Neural Automatic Post-Editing—0
Crowd-sourced Phrase-Based Tokenization for Low-Resourced Neural Machine Translation: The case of Fon Language—0
Crowdsourced Phrase-Based Tokenization for Low-Resourced Neural Machine Translation: The Case of Fon Language—0
CSP:Code-Switching Pre-training for Neural Machine Translation—0
CUNI English-Czech and English-Polish Systems in WMT20: Robust Document-Level Training—0
CUNI NMT System for WAT 2017 Translation Tasks—0
CUNI NMT System for WAT 2018 Translation Tasks—0
CUNI System for the Building Educational Applications 2019 Shared Task: Grammatical Error Correction—0
CUNI Systems in WMT21: Revisiting Backtranslation Techniques for English-Czech NMT—0
CUNI Transformer Neural MT System for WMT18—0
Current Shortcomings of Machine Translation in Spanish and Bulgarian Vis-à-vis English—0
Curriculum Learning and Minibatch Bucketing in Neural Machine Translation—0
Cyber Risks of Machine Translation Critical Errors : Arabic Mental Health Tweets as a Case Study—0
DaLC: Domain Adaptation Learning Curve Prediction for Neural Machine Translation—0
Data Augmentation for Sign Language Gloss Translation—0
Data Augmentation to Address Out-of-Vocabulary Problem in Low-Resource Sinhala-English Neural Machine Translation—0
Data augmentation using back-translation for context-aware neural machine translation—0
Data Augmentation With Back translation for Low Resource languages: A case of English and Luganda—0
Data Scaling Laws in NMT: The Effect of Noise and Architecture—0
Data Selection Curriculum for Neural Machine Translation—0
Data Selection with Feature Decay Algorithms Using an Approximated Target Side—0
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