10 papers · 1 filter
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…
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…
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…
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…