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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 601–650 of 1773 papers

TitleStatusHype
Neural Machine Translation with Heterogeneous Topic Knowledge EmbeddingsCode0
Contrastive Learning for Context-aware Neural Machine Translation Using Coreference Information—0
Self-Supervised Curriculum Learning for Spelling Error Correction—0
Adaptive Bridge between Training and Inference for Dialogue Generation—0
FJWU Participation for the WMT21 Biomedical Translation Task—0
Fix-Filter-Fix: Intuitively Connect Any Models for Effective Bug Fixing—0
TransIns: Document Translation with Markup ReinsertionCode0
Wino-X: Multilingual Winograd Schemas for Commonsense Reasoning and Coreference ResolutionCode0
Recurrent Attention for Neural Machine TranslationCode0
The TALP-UPC Participation in WMT21 News Translation Task: an mBART-based NMT Approach—0
Illinois Japanese English News Translation for WMT 2021—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
NITK-UoH: Tamil-Telugu Machine Translation Systems for the WMT21 Similar Language Translation Task—0
Encouraging Lexical Translation Consistency for Document-Level Neural Machine Translation—0
How should human translation coexist with NMT? Efficient tool for building high quality parallel corpus—0
Empirical Analysis of Korean Public AI Hub Parallel Corpora and in-depth Analysis using LIWC—0
Adaptive Bridge between Training and Inference for Dialogue—0
Continual Learning in Multilingual NMT via Language-Specific Embeddings—0
Multilingual Domain Adaptation for NMT: Decoupling Language and Domain Information with Adapters—0
Can Synthetic Translations Improve Bitext Quality?—0
A Two-Stage Curriculum Training Framework for NMT—0
Multilingual Neural Machine Translation:Can Linguistic Hierarchies Help?—0
An Empirical Investigation of Multi-bridge Multilingual NMT models—0
Doubly-Trained Adversarial Data Augmentation for Neural Machine TranslationCode0
Sicilian Translator: A Recipe for Low-Resource NMTCode0
Improving Zero-shot Multilingual Neural Machine Translation for Low-Resource Languages—0
Prose2Poem: The Blessing of Transformers in Translating Prose to Persian PoetryCode0
EdinSaar@WMT21: North-Germanic Low-Resource Multilingual NMT—0
TransSlowDown: Efficiency Attacks on Neural Machine Translation Systems—0
On the Effectiveness of Quasi Character-Level Models for Machine Translation—0
Towards Reinforcement Learning for Pivot-based Neural Machine Translation with Non-autoregressive Transformer—0
Faithful Target Attribute Prediction in Neural Machine TranslationCode0
Non-Parametric Online Learning from Human Feedback for Neural Machine TranslationCode0
Learning Domain Specific Language Models for Automatic Speech Recognition through Machine Translation—0
Digging Errors in NMT: Evaluating and Understanding Model Errors from Hypothesis Distribution—0
Contrastive Word Embedding Learning for Neural Machine Translation—0
Towards Full Utilization on Mask Task for Distilling PLMs into NMT—0
Modeling Future for Neural Machine Translation by Fusing Target Information—0
Modeling Multi-granularity Segmentation for Rare Words in Neural Machine Translation—0
Translation Transformers Rediscover Inherent Data DomainsCode0
Scaling Laws for Neural Machine Translation—0
Efficient Inference for Multilingual Neural Machine Translation—0
Contrastive Learning for Context-aware Neural Machine TranslationUsing Coreference Information—0
Attention Weights in Transformer NMT Fail Aligning Words Between Sequences but Largely Explain Model Predictions—0
Multi-Sentence Resampling: A Simple Approach to Alleviate Dataset Length Bias and Beam-Search DegradationCode0
Fine Grained Human Evaluation for English-to-Chinese Machine Translation: A Case Study on Scientific Text—0
Modeling Concentrated Cross-Attention for Neural Machine Translation with Gaussian Mixture Model—0
Rule-based Morphological Inflection Improves Neural Terminology TranslationCode0
Neural Machine Translation Quality and Post-Editing PerformanceCode0
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