22 citations · 52 across the 21 of their papers we have counts for
21 papers
Slides2MindMap: Reconstructing Cognitively Efficient Knowledge Hierarchies from Lecture Slides
Yuzhi Wang, Rongjun Ye, Shengyuan Chen +5
Generating mind maps from lecture slides can help learners efficiently assimilate fragmented knowledge, promising substantial benefits for intelligent education. However, dedicated…
Solving Formal Math Problems by Decomposition and Iterative Reflection
Yichi Zhou, Jianqiu Zhao, Yongxin Zhang +14
General-purpose Large Language Models (LLMs) have achieved remarkable success in intelligence, performing comparably to human experts on complex reasoning tasks such as coding and…
FinTeam: A Multi-Agent Collaborative Intelligence System for Comprehensive Financial Scenarios
Yingqian Wu, Qiushi Wang, Zefei Long +7
Financial report generation tasks range from macro- to micro-economics analysis, also requiring extensive data analysis. Existing LLM models are usually fine-tuned on simple QA tas…
Multi-agent KTO: Reinforcing Strategic Interactions of Large Language Model in Language Game
Rong Ye, Yongxin Zhang, Yikai Zhang +3
Achieving Artificial General Intelligence (AGI) requires AI agents that can not only make stratigic decisions but also engage in flexible and meaningful communication. Inspired by…
AgentSense: Benchmarking Social Intelligence of Language Agents through Interactive Scenarios
Xinyi Mou, Jingcong Liang, Jiayu Lin +8
Large language models (LLMs) are increasingly leveraged to empower autonomous agents to simulate human beings in various fields of behavioral research. However, evaluating their ca…
Debatrix: Multi-dimensional Debate Judge with Iterative Chronological Analysis Based on LLM
Jingcong Liang, Rong Ye, Meng Han +4
How can we construct an automated debate judge to evaluate an extensive, vibrant, multi-turn debate? This task is challenging, as judging a debate involves grappling with lengthy t…
Argue with Me Tersely: Towards Sentence-Level Counter-Argument Generation
Jiayu Lin, Rong Ye, Meng Han +6
Counter-argument generation -- a captivating area in computational linguistics -- seeks to craft statements that offer opposing views. While most research has ventured into paragra…
Speech Translation with Large Language Models: An Industrial Practice
Zhichao Huang, Rong Ye, Tom Ko +4
Given the great success of large language models (LLMs) across various tasks, in this paper, we introduce LLM-ST, a novel and effective speech translation model constructed upon a…
Hi-ArG: Exploring the Integration of Hierarchical Argumentation Graphs in Language Pretraining
Jingcong Liang, Rong Ye, Meng Han +6
The knowledge graph is a structure to store and represent knowledge, and recent studies have discussed its capability to assist language models for various applications. Some varia…
Put Your Money Where Your Mouth Is: Evaluating Strategic Planning and Execution of LLM Agents in an Auction Arena
Jiangjie Chen, Siyu Yuan, Rong Ye +2
Recent advancements in Large Language Models (LLMs) showcase advanced reasoning, yet NLP evaluations often depend on static benchmarks. Evaluating this necessitates environments th…
Recent Advances in Direct Speech-to-text Translation
Chen Xu, Rong Ye, Qianqian Dong +5
Recently, speech-to-text translation has attracted more and more attention and many studies have emerged rapidly. In this paper, we present a comprehensive survey on direct speech…
Improving speech translation by fusing speech and text
Wenbiao Yin, Zhicheng Liu, Chengqi Zhao +3
In speech translation, leveraging multimodal data to improve model performance and address limitations of individual modalities has shown significant effectiveness. In this paper,…
DUB: Discrete Unit Back-translation for Speech Translation
Dong Zhang, Rong Ye, Tom Ko +2
How can speech-to-text translation (ST) perform as well as machine translation (MT)? The key point is to bridge the modality gap between speech and text so that useful MT technique…
WACO: Word-Aligned Contrastive Learning for Speech Translation
Siqi Ouyang, Rong Ye, Lei Li
End-to-end Speech Translation (E2E ST) aims to directly translate source speech into target text. Existing ST methods perform poorly when only extremely small speech-text data are…
On the Impact of Noises in Crowd-Sourced Data for Speech Translation
Siqi Ouyang, Rong Ye, Lei Li
Training speech translation (ST) models requires large and high-quality datasets. MuST-C is one of the most widely used ST benchmark datasets. It contains around 400 hours of speec…
Cross-modal Contrastive Learning for Speech Translation
Rong Ye, Mingxuan Wang, Lei Li
How can we learn unified representations for spoken utterances and their written text? Learning similar representations for semantically similar speech and text is important for sp…
GigaST: A 10,000-hour Pseudo Speech Translation Corpus
Rong Ye, Chengqi Zhao, Tom Ko +4
This paper introduces GigaST, a large-scale pseudo speech translation (ST) corpus. We create the corpus by translating the text in GigaSpeech, an English ASR corpus, into German an…
STEMM: Self-learning with Speech-text Manifold Mixup for Speech Translation
Qingkai Fang, Rong Ye, Lei Li +2
How to learn a better speech representation for end-to-end speech-to-text translation (ST) with limited labeled data? Existing techniques often attempt to transfer powerful machine…
The Volctrans Neural Speech Translation System for IWSLT 2021
Chengqi Zhao, Zhicheng Liu, Jian Tong +6
This paper describes the systems submitted to IWSLT 2021 by the Volctrans team. We participate in the offline speech translation and text-to-text simultaneous translation tracks. F…
End-to-end Speech Translation via Cross-modal Progressive Training
Rong Ye, Mingxuan Wang, Lei Li
End-to-end speech translation models have become a new trend in research due to their potential of reducing error propagation. However, these models still suffer from the challenge…
Variational Template Machine for Data-to-Text Generation
Rong Ye, Wenxian Shi, Hao Zhou +2
How to generate descriptions from structured data organized in tables? Existing approaches using neural encoder-decoder models often suffer from lacking diversity. We claim that an…