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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 551–600 of 1773 papers

TitleStatusHype
Nearest Neighbor Knowledge Distillation for Neural Machine Translation—0
What Role Does BERT Play in the Neural Machine Translation Encoder?—0
When do Contrastive Word Alignments Improve Many-to-many Neural Machine Translation?—0
Cost-Effective Training in Low-Resource Neural Machine Translation—0
PAEG: Phrase-level Adversarial Example Generation for Neural Machine Translation—0
SMDT: Selective Memory-Augmented Neural Document Translation—0
ViNMT: Neural Machine Translation ToolkitCode0
How do lexical semantics affect translation? An empirical studyCode0
Frequency-Aware Contrastive Learning for Neural Machine Translation—0
A Preordered RNN Layer Boosts Neural Machine Translation in Low Resource Settings—0
Joint-training on Symbiosis Networks for Deep Nueral Machine Translation models—0
Spiral Language Modeling—0
Amortized Noisy Channel Neural Machine Translation—0
Harnessing Cross-lingual Features to Improve Cognate Detection for Low-resource LanguagesCode0
Improving Both Domain Robustness and Domain Adaptability in Machine TranslationCode0
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
Causal Transformers: Improving the Robustness on Spurious Correlations—0
A Primer in NMTology: What we have understood about NMT—0
Unified NMT models for the Indian subcontinent transcending script-barriers—0
Research on the Evaluation of Token Imbalance Degree of NMT Corpus—0
A Neural Approach to KGQA via SPARQL Silhouette Generation—0
Dict-NMT: Bilingual Dictionary based NMT for Extremely Low Resource Languages—0
Quality-Aware Decoding for Neural Machine Translation—0
An Empirical Study of Document-to-document Neural Machine Translation—0
Integrating Vectorized Lexical Constraints for Neural Machine Translation—0
Cost-Effective Training in Low-Resource Neural Machine Translation—0
IndicBART: A Pre-trained Model for Indic Natural Language Generation—0
Understanding and Improving Sequence-to-Sequence Pretraining for Neural Machine Translation—0
What Works and Doesn't Work, A Deep Decoder for Neural Machine Translation—0
Clause Attention based on Signal Words Division—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
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
Self-Supervised Curriculum Learning for Spelling Error Correction—0
Fix-Filter-Fix: Intuitively Connect Any Models for Effective Bug Fixing—0
Multilingual Neural Machine Translation: Can Linguistic Hierarchies Help?—0
Recurrent Attention for Neural Machine TranslationCode0
FJWU Participation for the WMT21 Biomedical Translation Task—0
Neural Machine Translation with Heterogeneous Topic Knowledge EmbeddingsCode0
Two Heads are Better than One? Verification of Ensemble Effect in Neural Machine Translation—0
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