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Found 3 papers, 3 papers with code
Simul-LLM: A Framework for Exploring High-Quality Simultaneous Translation with Large Language Models
Large language models (LLMs) with billions of parameters and pretrained on massive amounts of data are now capable of near or better than state-of-the-art performance in a variety of downstream natural language processing tasks.
Implicit Memory Transformer for Computationally Efficient Simultaneous Speech Translation
Experiments on the MuST-C dataset show that the Implicit Memory Transformer provides a substantial speedup on the encoder forward pass with nearly identical translation quality when compared with the state-of-the-art approach that employs both left context and memory banks.
Shiftable Context: Addressing Training-Inference Context Mismatch in Simultaneous Speech Translation
Transformer models using segment-based processing have been an effective architecture for simultaneous speech translation.
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