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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 976–1000 of 1773 papers

TitleStatusHype
Relevance-guided Neural Machine Translation—0
Repairing Pronouns in Translation with BERT-Based Post-Editing—0
Reproducible and Efficient Benchmarks for Hyperparameter Optimization of Neural Machine Translation Systems—0
Research on the Evaluation of Token Imbalance Degree of NMT Corpus—0
Residual Stacking of RNNs for Neural Machine Translation—0
Digging Errors in NMT: Evaluating and Understanding Model Errors from Partial Hypothesis Space—0
Rethinking Translation Memory Augmented Neural Machine Translation—0
Retrosynthesis with Attention-Based NMT Model and Chemical Analysis of the "Wrong" Predictions—0
Revisiting Character-Based Neural Machine Translation with Capacity and Compression—0
Revisiting Low Resource Status of Indian Languages in Machine Translation—0
Revisiting Modularized Multilingual NMT to Meet Industrial Demands—0
Revisiting Neural Scaling Laws in Language and Vision—0
Revisiting Robust Neural Machine Translation: A Transformer Case Study—0
Revisiting Round-Trip Translation for Quality Estimation—0
Rewriter-Evaluator Framework for Neural Machine Translation—0
Rewriter-Evaluator Architecture for Neural Machine Translation—0
RMBR: A Regularized Minimum Bayes Risk Reranking Framework for Machine Translation—0
Robust Neural Machine Translation for Clean and Noisy Speech Transcripts—0
Robust Neural Machine Translation with Doubly Adversarial Inputs—0
Robust Neural Machine Translation with Joint Textual and Phonetic Embedding—0
RTM results for Predicting Translation Performance—0
SALTED: A Framework for SAlient Long-Tail Translation Error Detection—0
Samsung R&D Institute Poland submission to WAT 2021 Indic Language Multilingual Task—0
Sarah's Participation in WAT 2019—0
Scalable Transformers for Neural Machine Translation—0
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