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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 451–500 of 1773 papers

TitleStatusHype
Communication-Efficient Federated Learning for Neural Machine Translation—0
Skeletal Graph Self-Attention: Embedding a Skeleton Inductive Bias into Sign Language Production—0
EduMT: Developing Machine Translation System for Educational Content in Indian Languages—0
Named Entity-Factored Transformer for Proper Noun Translation—0
Are Ellipses Important for Machine Translation?—0
Improvement in Machine Translation with Generative Adversarial Networks—0
Deps-SAN: Neural Machine Translation with Dependency-Scaled Self-Attention Network—0
Integrating Vectorized Lexical Constraints for Neural Machine Translation—0
Dict-NMT: Bilingual Dictionary based NMT for Extremely Low Resource Languages—0
IndicBART: A Pre-trained Model for Indic Natural Language Generation—0
What Works and Doesn't Work, A Deep Decoder for Neural Machine Translation—0
Causal Transformers: Improving the Robustness on Spurious Correlations—0
A Primer in NMTology: What we have understood about NMT—0
Understanding and Improving Sequence-to-Sequence Pretraining for Neural Machine Translation—0
Generating Authentic Adversarial Examples beyond Meaning-preserving with Doubly Round-trip Translation—0
Enhancing the Nonlinear Mutual Dependencies in Transformers with Mutual Information—0
A Neural Approach to KGQA via SPARQL Silhouette Generation—0
Research on the Evaluation of Token Imbalance Degree of NMT Corpus—0
An Empirical Study of Document-to-document Neural Machine Translation—0
Quality-Aware Decoding for Neural Machine Translation—0
Cost-Effective Training in Low-Resource Neural Machine Translation—0
Clause Attention based on Signal Words Division—0
Unified NMT models for the Indian subcontinent transcending script-barriers—0
Sign Language Translation with Hierarchical Spatio-TemporalGraph Neural Network—0
BitextEdit: Automatic Bitext Editing for Improved Low-Resource Machine Translation—0
Developing neural machine translation models for Hungarian-English—0
Zero-Shot Translation using Diffusion Models—0
The TALP-UPC Participation in WMT21 News Translation Task: an mBART-based NMT Approach—0
Similar Language Translation for Catalan, Portuguese and Spanish Using Marian NMT—0
NITK-UoH: Tamil-Telugu Machine Translation Systems for the WMT21 Similar Language Translation Task—0
CUNI Systems in WMT21: Revisiting Backtranslation Techniques for English-Czech NMT—0
FJWU Participation for the WMT21 Biomedical Translation Task—0
Illinois Japanese English News Translation for WMT 2021—0
Contrastive Learning for Context-aware Neural Machine Translation Using Coreference Information—0
Improving Machine Translation of Rare and Unseen Word Senses—0
eTranslation’s Submissions to the WMT 2021 News Translation Task—0
Adam Mickiewicz University’s English-Hausa Submissions to the WMT 2021 News Translation Task—0
Syntax in End-to-End Natural Language Processing—0
Multi-Class Grammatical Error Detection for Correction: A Tale of Two SystemsCode0
Wino-X: Multilingual Winograd Schemas for Commonsense Reasoning and Coreference ResolutionCode0
Two Heads are Better than One? Verification of Ensemble Effect in Neural Machine Translation—0
TransIns: Document Translation with Markup ReinsertionCode0
Neural Machine Translation with Heterogeneous Topic Knowledge EmbeddingsCode0
Encouraging Lexical Translation Consistency for Document-Level Neural Machine Translation—0
Fix-Filter-Fix: Intuitively Connect Any Models for Effective Bug Fixing—0
Recurrent Attention for Neural Machine TranslationCode0
Adaptive Bridge between Training and Inference for Dialogue Generation—0
Self-Supervised Curriculum Learning for Spelling Error Correction—0
A Large-Scale Study of Machine Translation in Turkic LanguagesCode1
Sometimes We Want Ungrammatical TranslationsCode0
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