most citedNon-autoregressive Machine Translation with Probabilistic Context-free Grammar

8 citations · 10 across the 8 of their papers we have counts for

collaborators

8 papers

cs.CL2024

Agent-SiMT: Agent-assisted Simultaneous Machine Translation with Large Language Models

Shoutao Guo, Shaolei Zhang, Zhengrui Ma +2

Simultaneous Machine Translation (SiMT) generates target translations while reading the source sentence. It relies on a policy to determine the optimal timing for reading sentences…

cs.CL2024

CTC-based Non-autoregressive Textless Speech-to-Speech Translation

Qingkai Fang, Zhengrui Ma, Yan Zhou +2

Direct speech-to-speech translation (S2ST) has achieved impressive translation quality, but it often faces the challenge of slow decoding due to the considerable length of speech s…

cs.CL2024

Can We Achieve High-quality Direct Speech-to-Speech Translation without Parallel Speech Data?

Qingkai Fang, Shaolei Zhang, Zhengrui Ma +2

Recently proposed two-pass direct speech-to-speech translation (S2ST) models decompose the task into speech-to-text translation (S2TT) and text-to-speech (TTS) within an end-to-end…

cs.CL2024

StreamSpeech: Simultaneous Speech-to-Speech Translation with Multi-task Learning

Shaolei Zhang, Qingkai Fang, Shoutao Guo +3

Simultaneous speech-to-speech translation (Simul-S2ST, a.k.a streaming speech translation) outputs target speech while receiving streaming speech inputs, which is critical for real…

cs.CL20242 cited

SiLLM: Large Language Models for Simultaneous Machine Translation

Shoutao Guo, Shaolei Zhang, Zhengrui Ma +2

Simultaneous Machine Translation (SiMT) generates translations while reading the source sentence, necessitating a policy to determine the optimal timing for reading and generating…

cs.CL20238 cited

Non-autoregressive Machine Translation with Probabilistic Context-free Grammar

Shangtong Gui, Chenze Shao, Zhengrui Ma +3

Non-autoregressive Transformer(NAT) significantly accelerates the inference of neural machine translation. However, conventional NAT models suffer from limited expression power and…