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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 221–230 of 1773 papers

TitleStatusHype
HW-TSC's Submission to the CCMT 2024 Machine Translation Tasks—0
Choose the Final Translation from NMT and LLM hypotheses Using MBR Decoding: HW-TSC's Submission to the WMT24 General MT Shared Task—0
EMMeTT: Efficient Multimodal Machine Translation Training—0
Thesis proposal: Are We Losing Textual Diversity to Natural Language Processing?—0
Vision-fused Attack: Advancing Aggressive and Stealthy Adversarial Text against Neural Machine TranslationCode0
N-gram Prediction and Word Difference Representations for Language Modeling—0
Integrating Multi-Head Convolutional Encoders with Cross-Attention for Improved SPARQL Query Translation—0
Quality or Quantity? On Data Scale and Diversity in Adapting Large Language Models for Low-Resource Translation—0
High-Quality Data Augmentation for Low-Resource NMT: Combining a Translation Memory, a GAN Generator, and Filtering—0
Defining Boundaries: The Impact of Domain Specification on Cross-Language and Cross-Domain Transfer in Machine Translation—0
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