8 citations · 21 across the 33 of their papers we have counts for
11 papers · 2 filters
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…
Align-then-Slide: A complete evaluation framework for Ultra-Long Document-Level Machine Translation
Jiaxin Guo, Daimeng Wei, Yuanchang Luo +8
Large language models (LLMs) have ushered in a new era for document-level machine translation (\textit{doc}-mt), yet their whole-document outputs challenge existing evaluation meth…
Generative Annotation for ASR Named Entity Correction
Yuanchang Luo, Daimeng Wei, Shaojun Li +8
End-to-end automatic speech recognition systems often fail to transcribe domain-specific named entities, causing catastrophic failures in downstream tasks. Numerous fast and lightw…
Combining the Best of Both Worlds: A Method for Hybrid NMT and LLM Translation
Zhanglin Wu, Daimeng Wei, Xiaoyu Chen +7
Large language model (LLM) shows promising performances in a variety of downstream tasks, such as machine translation (MT). However, using LLMs for translation suffers from high co…
Automatic Evaluation Metrics for Document-level Translation: Overview, Challenges and Trends
Jiaxin GUO, Xiaoyu Chen, Zhiqiang Rao +5
With the rapid development of deep learning technologies, the field of machine translation has witnessed significant progress, especially with the advent of large language models (…
Two Intermediate Translations Are Better Than One: Fine-tuning LLMs for Document-level Translation Refinement
Yichen Dong, Xinglin Lyu, Junhui Li +4
Recent research has shown that large language models (LLMs) can enhance translation quality through self-refinement. In this paper, we build on this idea by extending the refinemen…
DoCIA: An Online Document-Level Context Incorporation Agent for Speech Translation
Xinglin Lyu, Wei Tang, Yuang Li +7
Document-level context is crucial for handling discourse challenges in text-to-text document-level machine translation (MT). Despite the increased discourse challenges introduced b…
Improving LLM-based Document-level Machine Translation with Multi-Knowledge Fusion
Bin Liu, Xinglin Lyu, Junhui Li +4
Recent studies in prompting large language model (LLM) for document-level machine translation (DMT) primarily focus on the inter-sentence context by flatting the source document in…
Chain-of-Description: What I can understand, I can put into words
Jiaxin Guo, Daimeng Wei, Zongyao Li +3
In this paper, we propose a novel strategy defined as Chain-of-Description (CoD) Prompting, tailored for Multi-Modal Large Language Models. This approach involves having the model…
R1-T1: Fully Incentivizing Translation Capability in LLMs via Reasoning Learning
Minggui He, Yilun Liu, Shimin Tao +10
Despite recent breakthroughs in reasoning-enhanced large language models (LLMs) like DeepSeek-R1, incorporating inference-time reasoning into machine translation (MT), where human…
Doc-Guided Sent2Sent++: A Sent2Sent++ Agent with Doc-Guided memory for Document-level Machine Translation
Jiaxin Guo, Yuanchang Luo, Daimeng Wei +8
The field of artificial intelligence has witnessed significant advancements in natural language processing, largely attributed to the capabilities of Large Language Models (LLMs).…