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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 861–870 of 1773 papers

TitleStatusHype
Lattice-Based Recurrent Neural Network Encoders for Neural Machine Translation—0
Lattice-Based Transformer Encoder for Neural Machine Translation—0
Layer-Wise Coordination between Encoder and Decoder for Neural Machine Translation—0
Evaluating the Supervised and Zero-shot Performance of Multi-lingual Translation Models—0
Learning Contextualized Sentence Representations for Document-Level Neural Machine Translation—0
Learning Distributional Token Representations from Visual Features—0
Evaluating the Performance of Back-translation for Low Resource English-Marathi Language Pair: CFILT-IITBombay @ LoResMT 2021—0
Learning Efficient Lexically-Constrained Neural Machine Translation with External Memory—0
Learning Feature Weights using Reward Modeling for Denoising Parallel Corpora—0
C-NMT: A Collaborative Inference Framework for Neural Machine Translation—0
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