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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 81–90 of 1773 papers

TitleStatusHype
Enhancing Language Learning through Technology: Introducing a New English-Azerbaijani (Arabic Script) Parallel Corpus—0
Language Portability Strategies for Open-domain Dialogue with Pre-trained Language Models from High to Low Resource Languages—0
Unveiling the Power of Source: Source-based Minimum Bayes Risk Decoding for Neural Machine Translation—0
M3T: A New Benchmark Dataset for Multi-Modal Document-Level Machine TranslationCode0
TasTe: Teaching Large Language Models to Translate through Self-ReflectionCode1
Efficient k-Nearest-Neighbor Machine Translation with Dynamic RetrievalCode0
Understanding and Addressing the Under-Translation Problem from the Perspective of Decoding Objective—0
Integrating Multi-scale Contextualized Information for Byte-based Neural Machine TranslationCode0
Cyber Risks of Machine Translation Critical Errors : Arabic Mental Health Tweets as a Case Study—0
Reinforcement Learning for Edit-Based Non-Autoregressive Neural Machine Translation—0
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