Search Results for author: Zhengrui Ma

Found 14 papers, 11 papers with code

Prediction Difference Regularization against Perturbation for Neural Machine Translation

no code implementations ACL 2022 Dengji Guo, Zhengrui Ma, Min Zhang, Yang Feng

Regularization methods applying input perturbation have drawn considerable attention and have been frequently explored for NMT tasks in recent years.

Machine Translation NMT +1

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

1 code implementation11 Jun 2024 Qingkai Fang, Zhengrui Ma, Yan Zhou, Min Zhang, Yang Feng

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 sequences.

Knowledge Distillation Machine Translation +2

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

no code implementations11 Jun 2024 Qingkai Fang, Shaolei Zhang, Zhengrui Ma, Min Zhang, Yang Feng

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 model, yielding promising results.

Contrastive Learning Speech Synthesis +4

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

1 code implementation5 Jun 2024 Shaolei Zhang, Qingkai Fang, Shoutao Guo, Zhengrui Ma, Min Zhang, Yang Feng

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-time communication.

 Ranked #1 on de-en on CVSS

Automatic Speech Recognition (ASR) de-en +11

SiLLM: Large Language Models for Simultaneous Machine Translation

1 code implementation20 Feb 2024 Shoutao Guo, Shaolei Zhang, Zhengrui Ma, Min Zhang, Yang Feng

We propose SiLLM, which delegates the two sub-tasks to separate agents, thereby incorporating LLM into SiMT.

Machine Translation Sentence +1

Non-autoregressive Streaming Transformer for Simultaneous Translation

1 code implementation23 Oct 2023 Zhengrui Ma, Shaolei Zhang, Shoutao Guo, Chenze Shao, Min Zhang, Yang Feng

Simultaneous machine translation (SiMT) models are trained to strike a balance between latency and translation quality.

Decoder Machine Translation +1

BayLing: Bridging Cross-lingual Alignment and Instruction Following through Interactive Translation for Large Language Models

1 code implementation19 Jun 2023 Shaolei Zhang, Qingkai Fang, Zhuocheng Zhang, Zhengrui Ma, Yan Zhou, Langlin Huang, Mengyu Bu, Shangtong Gui, Yunji Chen, Xilin Chen, Yang Feng

To minimize human workload, we propose to transfer the capabilities of language generation and instruction following from English to other languages through an interactive translation task.

Instruction Following Text Generation +1

Fuzzy Alignments in Directed Acyclic Graph for Non-Autoregressive Machine Translation

1 code implementation12 Mar 2023 Zhengrui Ma, Chenze Shao, Shangtong Gui, Min Zhang, Yang Feng

Non-autoregressive translation (NAT) reduces the decoding latency but suffers from performance degradation due to the multi-modality problem.

Machine Translation Sentence +1

Viterbi Decoding of Directed Acyclic Transformer for Non-Autoregressive Machine Translation

1 code implementation11 Oct 2022 Chenze Shao, Zhengrui Ma, Yang Feng

Non-autoregressive models achieve significant decoding speedup in neural machine translation but lack the ability to capture sequential dependency.

Machine Translation Translation

Smoothed Multi-View Subspace Clustering

1 code implementation18 Jun 2021 Peng Chen, Liang Liu, Zhengrui Ma, Zhao Kang

In recent years, multi-view subspace clustering has achieved impressive performance due to the exploitation of complementary imformation across multiple views.

Clustering Multi-view Subspace Clustering

Towards Clustering-friendly Representations: Subspace Clustering via Graph Filtering

1 code implementation18 Jun 2021 Zhengrui Ma, Zhao Kang, Guangchun Luo, Ling Tian

The success of subspace clustering depends on the assumption that the data can be separated into different subspaces.

Clustering Graph Similarity

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