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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 951–1000 of 1773 papers

TitleStatusHype
Question Answering over Knowledge Graphs with Neural Machine Translation and Entity Linking—0
Rank-One Editing of Encoder-Decoder Models—0
Reconsidering SMT Over NMT for Closely Related Languages: A Case Study of Persian-Hindi Pair—0
Recurrent Graph Syntax Encoder for Neural Machine Translation—0
Recurrent Stacking of Layers for Compact Neural Machine Translation Models—0
Recurrent Stacking of Layers in Neural Networks: An Application to Neural Machine Translation—0
Recycling a Pre-trained BERT Encoder for Neural Machine Translation—0
Reduce Indonesian Vocabularies with an Indonesian Sub-word Separator—0
Reducing Disambiguation Biases in NMT by Leveraging Explicit Word Sense Information—0
Reducing Gender Bias in Machine Translation through Counterfactual Data Generation—0
Reducing Hallucinations in Neural Machine Translation with Feature Attribution—0
Reducing Length Bias in Scoring Neural Machine Translation via a Causal Inference Method—0
Reducing Word Omission Errors in Neural Machine Translation: A Contrastive Learning Approach—0
Reference Network for Neural Machine Translation—0
Refining Source Representations with Relation Networks for Neural Machine Translation—0
Refining the state-of-the-art in Machine Translation, optimizing NMT for the JA <-> EN language pair by leveraging personal domain expertise—0
Regressing Word and Sentence Embeddings for Regularization of Neural Machine Translation—0
Regularization techniques for fine-tuning in neural machine translation—0
Regularized Context Gates on Transformer for Machine Translation—0
Regularizing Neural Machine Translation by Target-bidirectional Agreement—0
Reinforced Curriculum Learning on Pre-trained Neural Machine Translation Models—0
Reinforcement Learning based Curriculum Optimization for Neural Machine Translation—0
Reinforcement Learning for Edit-Based Non-Autoregressive Neural Machine Translation—0
Reinforcement Learning with Large Action Spaces for Neural Machine Translation—0
Reinforcement Learning with Large Action Spaces for Neural Machine Translation—0
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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