arXiv:2004.05809
공식 코드 · Papers with Code
Neural Machine Translation: Challenges, Progress and Future
원문 초록
Machine translation (MT) is a technique that leverages computers to translate human languages automatically. Nowadays, neural machine translation (NMT) which models direct mapping between source and target languages with deep neural networks has achieved a big breakthrough in translation performance and become the de facto paradigm of MT. This article makes a review of NMT framework, discusses the challenges in NMT, introduces some exciting recent progresses and finally looks forward to some potential future research trends. In addition, we maintain the state-of-the-art methods for various NMT tasks at the website https://github.com/ZNLP/SOTA-MT.
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Do Not Change Me: On Transferring Entities Without Modification in Neural Machine Translation -- a Multilingual Perspective
Calibrating Translation Decoding with Quality Estimation on LLMs
Beyond Decoder-only: Large Language Models Can be Good Encoders for Machine Translation
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
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