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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 11–20 of 1773 papers

TitleStatusHype
AI agents may be worth the hype but not the resources (yet): An initial exploration of machine translation quality and costs in three language pairs in the legal and news domains—0
Calibrating Translation Decoding with Quality Estimation on LLMsCode0
Is LLM the Silver Bullet to Low-Resource Languages Machine Translation?—0
Beyond Vanilla Fine-Tuning: Leveraging Multistage, Multilingual, and Domain-Specific Methods for Low-Resource Machine Translation—0
Contextual Cues in Machine Translation: Investigating the Potential of Multi-Source Input Strategies in LLMs and NMT Systems—0
Beyond Decoder-only: Large Language Models Can be Good Encoders for Machine TranslationCode1
MultiSlav: Using Cross-Lingual Knowledge Transfer to Combat the Curse of Multilinguality—0
Quality-Aware Decoding: Unifying Quality Estimation and Decoding—0
Evaluation of NMT-Assisted Grammar Transfer for a Multi-Language Configurable Data-to-Text System—0
Faster Machine Translation Ensembling with Reinforcement Learning and Competitive Correction—0
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