9 papers
Cross-Preference Learning for Sentence-Level and Context-Aware Machine Translation
Ying Li, Xinglin Lyu, Junhui Li +5
Context-aware machine translation (MT) leverages document-level information, yet it does not consistently outperform sentence-level MT, as contextual signals are unevenly beneficia…
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…
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 (…
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).…