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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 901–950 of 1773 papers

TitleStatusHype
Using Context in Neural Machine Translation Training Objectives—0
Using Images to Improve Machine-Translating E-Commerce Product Listings.—0
Investigating Massive Multilingual Pre-Trained Machine Translation Models for Clinical Domain via Transfer Learning—0
Enhanced back-translation for low resource neural machine translation using self-training—0
Using Semantic Role Labeling to Improve Neural Machine Translation—0
Using Semantic Similarity as Reward for Reinforcement Learning in Sentence Generation—0
Using Spoken Word Posterior Features in Neural Machine Translation—0
Using Target-side Monolingual Data for Neural Machine Translation through Multi-task Learning—0
Utilizing Monolingual Data in NMT for Similar Languages: Submission to Similar Language Translation Task—0
Validation of Tsallis Entropy In Inter-Modality Neuroimage Registration—0
Variational Neural Machine Translation with Normalizing Flows—0
Variational Recurrent Neural Machine Translation—0
Vector-Vector-Matrix Architecture: A Novel Hardware-Aware Framework for Low-Latency Inference in NLP Applications—0
Visualizing and Understanding Neural Machine Translation—0
Vocabulary Adaptation for Distant Domain Adaptation in Neural Machine Translation—0
What Level of Quality can Neural Machine Translation Attain on Literary Text?—0
What Makes Word-level Neural Machine Translation Hard: A Case Study on English-German Translation—0
What Role Does BERT Play in the Neural Machine Translation Encoder?—0
What Works and Doesn't Work, A Deep Decoder for Neural Machine Translation—0
What Works and Doesn’t Work, A Deep Decoder for Neural Machine Translation—0
When and Why is Unsupervised Neural Machine Translation Useless?—0
When a `sport' is a person and other issues for NMT of novels—0
When do Contrastive Word Alignments Improve Many-to-many Neural Machine Translation?—0
When do Contrastive Word Alignments Improve Many-to-many Neural Machine Translation?—0
When is Char Better Than Subword: A Systematic Study of Segmentation Algorithms for Neural Machine Translation—0
Who Evaluates the Evaluators? On Automatic Metrics for Assessing AI-based Offensive Code Generators—0
Why Find the Right One?—0
Why Neural Machine Translation Prefers Empty Outputs—0
Word Alignment in the Era of Deep Learning: A Tutorial—0
Word Rewarding for Adequate Neural Machine Translation—0
WT: Wipro AI Submissions to the WAT 2020—0
XMU Neural Machine Translation Online Service—0
xSIM++: An Improved Proxy to Bitext Mining Performance for Low-Resource Languages—0
YANMTT: Yet Another Neural Machine Translation Toolkit—0
Zero-Resource Neural Machine Translation with Multi-Agent Communication Game—0
Zero-Resource Neural Machine Translation with Monolingual Pivot Data—0
Zero-Shot Cross-lingual Classification Using Multilingual Neural Machine Translation—0
Zero-Shot Neural Machine Translation: Russian-Hindi @LoResMT 2020—0
Zero-Shot Neural Machine Translation with Self-Learning Cycle—0
Zero-shot translation among Indian languages—0
Zero-Shot Translation using Diffusion Models—0
Neural Machine Translation with Recurrent Highway Networks—0
FFR v1.1: Fon-French Neural Machine Translation—0
Filtering Back-Translated Data in Unsupervised Neural Machine Translation—0
Finding Sami Cognates with a Character-Based NMT Approach—0
Findings of the Fourth Workshop on Neural Generation and Translation—0
Findings of the WMT 2018 Shared Task on Automatic Post-Editing—0
Findings of the WMT 2020 Shared Task on Automatic Post-Editing—0
Finding the Right Recipe for Low Resource Domain Adaptation in Neural Machine Translation—0
Fine-Grained Attention Mechanism for Neural Machine Translation—0
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