arXiv:2002.07526
A Survey of Deep Learning Techniques for Neural Machine Translation
원문 초록
In recent years, natural language processing (NLP) has got great development with deep learning techniques. In the sub-field of machine translation, a new approach named Neural Machine Translation (NMT) has emerged and got massive attention from both academia and industry. However, with a significant number of researches proposed in the past several years, there is little work in investigating the development process of this new technology trend. This literature survey traces back the origin and principal development timeline of NMT, investigates the important branches, categorizes different research orientations, and discusses some future research trends in this field.
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Do Not Change Me: On Transferring Entities Without Modification in Neural Machine Translation -- a Multilingual Perspective
ConECT Dataset: Overcoming Data Scarcity in Context-Aware E-Commerce MT
In-Domain African Languages Translation Using LLMs and Multi-armed Bandits
THOR-MoE: Hierarchical Task-Guided and Context-Responsive Routing for Neural Machine Translation
Combining the Best of Both Worlds: A Method for Hybrid NMT and LLM Translation
TopicVD: A Topic-Based Dataset of Video-Guided Multimodal Machine Translation for Documentaries
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