Search Results for author: Rong Ye

Found 15 papers, 13 papers with code

Put Your Money Where Your Mouth Is: Evaluating Strategic Planning and Execution of LLM Agents in an Auction Arena

1 code implementation9 Oct 2023 Jiangjie Chen, Siyu Yuan, Rong Ye, Bodhisattwa Prasad Majumder, Kyle Richardson

Notably, even our most advanced models (GPT-4) are occasionally surpassed by heuristic baselines and human agents, highlighting the potential for further improvements in the design of LLM agents and the important role that our simulation environment can play in further testing and refining agent architectures.

Recent Advances in Direct Speech-to-text Translation

no code implementations20 Jun 2023 Chen Xu, Rong Ye, Qianqian Dong, Chengqi Zhao, Tom Ko, Mingxuan Wang, Tong Xiao, Jingbo Zhu

Recently, speech-to-text translation has attracted more and more attention and many studies have emerged rapidly.

Data Augmentation Knowledge Distillation +2

Improving speech translation by fusing speech and text

no code implementations23 May 2023 Wenbiao Yin, Zhicheng Liu, Chengqi Zhao, Tao Wang, Jian Tong, Rong Ye

To tackle these gaps, we propose \textbf{F}use-\textbf{S}peech-\textbf{T}ext (\textbf{FST}), a cross-modal model which supports three distinct input modalities for translation: speech, text, and fused speech-text.

Machine Translation Translation

DUB: Discrete Unit Back-translation for Speech Translation

1 code implementation19 May 2023 Dong Zhang, Rong Ye, Tom Ko, Mingxuan Wang, Yaqian Zhou

The key point is to bridge the modality gap between speech and text so that useful MT techniques can be applied to ST.

Machine Translation Speech-to-Text Translation +1

WACO: Word-Aligned Contrastive Learning for Speech Translation

1 code implementation19 Dec 2022 Siqi Ouyang, Rong Ye, Lei LI

In this paper, we propose Word-Aligned COntrastive learning (WACO), a simple and effective method for extremely low-resource speech-to-text translation.

Contrastive Learning Speech-to-Text Translation +1

Cross-modal Contrastive Learning for Speech Translation

1 code implementation NAACL 2022 Rong Ye, Mingxuan Wang, Lei LI

Learning similar representations for semantically similar speech and text is important for speech translation.

Contrastive Learning Retrieval +3

GigaST: A 10,000-hour Pseudo Speech Translation Corpus

1 code implementation8 Apr 2022 Rong Ye, Chengqi Zhao, Tom Ko, Chutong Meng, Tao Wang, Mingxuan Wang, Jun Cao

The training set is translated by a strong machine translation system and the test set is translated by human.

Machine Translation Test +1

The Volctrans Neural Speech Translation System for IWSLT 2021

1 code implementation ACL (IWSLT) 2021 Chengqi Zhao, Zhicheng Liu, Jian Tong, Tao Wang, Mingxuan Wang, Rong Ye, Qianqian Dong, Jun Cao, Lei LI

For offline speech translation, our best end-to-end model achieves 8. 1 BLEU improvements over the benchmark on the MuST-C test set and is even approaching the results of a strong cascade solution.


End-to-end Speech Translation via Cross-modal Progressive Training

1 code implementation21 Apr 2021 Rong Ye, Mingxuan Wang, Lei LI

XSTNet takes both speech and text as input and outputs both transcription and translation text.

Machine Translation Speech-to-Text Translation +1

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