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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 191–200 of 1773 papers

TitleStatusHype
Fine-Tuning MT systems for Robustness to Second-Language Speaker VariationsCode0
A Document-Level Neural Machine Translation Model with Dynamic Caching Guided by Theme-Rheme InformationCode0
First the worst: Finding better gender translations during beam searchCode0
From Priest to Doctor: Domain Adaptaion for Low-Resource Neural Machine TranslationCode0
Finding Better Subword Segmentation for Neural Machine TranslationCode0
Finding Memo: Extractive Memorization in Constrained Sequence Generation TasksCode0
A Copy Mechanism for Handling Knowledge Base Elements in SPARQL Neural Machine TranslationCode0
FGraDA: A Dataset and Benchmark for Fine-Grained Domain Adaptation in Machine TranslationCode0
Fine-grained Human Evaluation of Transformer and Recurrent Approaches to Neural Machine Translation for English-to-ChineseCode0
From the Paft to the Fiiture: a Fully Automatic NMT and Word Embeddings Method for OCR Post-CorrectionCode0
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