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20232026
most citedHow Well Do Large Reasoning Models Translate? A Comprehensive Evaluation for Multi-Domain Machine Translation

1 citations · 2 across the 11 of their papers we have counts for

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11 papers

cs.AI2026

Don't Peek at the Answer: Outcome-Masked Group Relative Policy Optimization for Label-Free RLVR

Yongshi Ye, Liang Zhang, Yidong Chen +2

Reinforcement Learning with Verifiable Rewards (RLVR) improves LLM reasoning but typically relies on ground-truth (GT) answers, limiting scalability. Voting-based label-free RLVR r…

cs.CL2026

PAMT: Process-Aligned Reinforcement Learning for Multi-Domain Machine Translation

Yongshi Ye, Biao Fu, Chongxuan Huang +2

Multi-domain machine translation (MDMT) requires more than fluent generation: it demands domain-sensitive translation decisions such as domain disambiguation, terminology control,…

cs.CL2026

Translation with Thought: Difficulty-Adaptive Reasoning via Reinforcement Learning for Multi-Domain Machine Translation

Yongshi Ye, Biao Fu, Chongxuan Huang +2

Multi-domain machine translation (MDMT) poses a unique challenge due to varying levels of linguistic complexity across domains. Inspired by human translators' ability to adapt reas…

cs.AI2026

Look on Demand: A Cognitive Scheduling Framework for Visual Evidence Acquisition in Multimodal Reasoning

Yang Zhang, Xiaoshuai Sun, Rui Zhao +5

Existing multimodal reasoning approaches predominantly follow two paradigms: converting visual inputs into text prior to reasoning, or performing end-to-end reasoning within a unif…

cs.CL2026

Selective Contrastive Learning For Gloss Free Sign Language Translation

Changhao Lai, Rui Zhao, Xuewen Zhong +2

Sign language translation (SLT) converts continuous sign videos into spoken-language text, yet it remains challenging due to the intrinsic modality mismatch between visual signs an…

cs.CL2026

CNSL-bench: Benchmarking the Sign Language Understanding Capabilities of MLLMs on Chinese National Sign Language

Rui Zhao, Xuewen Zhong, Xiaoyun Zheng +2

Sign language research has achieved significant progress due to the advances in large language models (LLMs). However, the intrinsic ability of LLMs to understand sign language, es…

cs.CL2025

Towards Fine-Grained Code-Switch Speech Translation with Semantic Space Alignment

Yan Gao, Yazheng Yang, Zhibin Lan +5

Code-switching (CS) speech translation (ST) aims to translate speech that alternates between multiple languages into a target language text, posing significant challenges due to th…

cs.CL2025

LLMs Can Achieve High-quality Simultaneous Machine Translation as Efficiently as Offline

Biao Fu, Minpeng Liao, Kai Fan +4

When the complete source sentence is provided, Large Language Models (LLMs) perform excellently in offline machine translation even with a simple prompt "Translate the following se…

cs.CL2025★ 1 cited

How Well Do Large Reasoning Models Translate? A Comprehensive Evaluation for Multi-Domain Machine Translation

Yongshi Ye, Biao Fu, Chongxuan Huang +2

Large language models (LLMs) have demonstrated strong performance in general-purpose machine translation, but their effectiveness in complex, domain-sensitive translation tasks rem…

cs.CL2025

Efficient and Adaptive Simultaneous Speech Translation with Fully Unidirectional Architecture

Biao Fu, Donglei Yu, Minpeng Liao +5

Simultaneous speech translation (SimulST) produces translations incrementally while processing partial speech input. Although large language models (LLMs) have shown strong capabil…

cs.CL2023★ 1 cited

Conditional Variational Autoencoder for Sign Language Translation with Cross-Modal Alignment

Rui Zhao, Liang Zhang, Biao Fu +3

Sign language translation (SLT) aims to convert continuous sign language videos into textual sentences. As a typical multi-modal task, there exists an inherent modality gap between…