Zobrazeno 1 - 10
of 21 543
pro vyhledávání: '"YANG, Hao"'
Autor:
Guo, Cong, Cheng, Feng, Du, Zhixu, Kiessling, James, Ku, Jonathan, Li, Shiyu, Li, Ziru, Ma, Mingyuan, Molom-Ochir, Tergel, Morris, Benjamin, Shan, Haoxuan, Sun, Jingwei, Wang, Yitu, Wei, Chiyue, Wu, Xueying, Wu, Yuhao, Yang, Hao Frank, Zhang, Jingyang, Zhang, Junyao, Zheng, Qilin, Zhou, Guanglei, Hai, Li, Chen, Yiran
The rapid development of large language models (LLMs) has significantly transformed the field of artificial intelligence, demonstrating remarkable capabilities in natural language processing and moving towards multi-modal functionality. These models
Externí odkaz:
http://arxiv.org/abs/2410.07265
Instability in supernova fallback disks and its effect on the formation of ultra long period pulsars
Several pulsars with unusually long periods were discovered recently, comprising a potential population of ultra long period pulsars (ULPPs). The origin of their long periodicity is not well understood, but may be related to magnatars spun down by su
Externí odkaz:
http://arxiv.org/abs/2410.05944
Haptic feedback to the surgeon during robotic surgery would enable safer and more immersive surgeries but estimating tissue interaction forces at the tips of robotically controlled surgical instruments has proven challenging. Few existing surgical ro
Externí odkaz:
http://arxiv.org/abs/2409.19970
Autor:
Yang, Jinlong, Shang, Hengchao, Wei, Daimeng, Guo, Jiaxin, Li, Zongyao, Wu, Zhanglin, Rao, Zhiqiang, Li, Shaojun, Xie, Yuhao, Luo, Yuanchang, Zheng, Jiawei, Wei, Bin, Yang, Hao
This paper describes the submissions of Huawei Translation Services Center(HW-TSC) to WMT24 chat translation shared task on English$\leftrightarrow$Germany (en-de) bidirection. The experiments involved fine-tuning models using chat data and exploring
Externí odkaz:
http://arxiv.org/abs/2409.16331
Autor:
Luo, Yuanchang, Wu, Zhanglin, Wei, Daimeng, Shang, Hengchao, Li, Zongyao, Guo, Jiaxin, Rao, Zhiqiang, Li, Shaojun, Yang, Jinlong, Xie, Yuhao, Wei, Jiawei Zheng Bin, Yang, Hao
This article introduces the submission status of the Translation into Low-Resource Languages of Spain task at (WMT 2024) by Huawei Translation Service Center (HW-TSC). We participated in three translation tasks: spanish to aragonese (es-arg), spanish
Externí odkaz:
http://arxiv.org/abs/2409.15924
Autor:
Wei, Bin, Zhen, Jiawei, Li, Zongyao, Wu, Zhanglin, Wei, Daimeng, Guo, Jiaxin, Rao, Zhiqiang, Li, Shaojun, Luo, Yuanchang, Shang, Hengchao, Yang, Jinlong, Xie, Yuhao, Yang, Hao
This paper introduces the submission by Huawei Translation Center (HW-TSC) to the WMT24 Indian Languages Machine Translation (MT) Shared Task. To develop a reliable machine translation system for low-resource Indian languages, we employed two distinc
Externí odkaz:
http://arxiv.org/abs/2409.15879
Autor:
Luo, Yuanchang, Guo, Jiaxin, Wei, Daimeng, Shang, Hengchao, Li, Zongyao, Wu, Zhanglin, Rao, Zhiqiang, Li, Shaojun, Yang, Jinlong, Yang, Hao
This report outlines our approach for the WMT24 Discourse-Level Literary Translation Task, focusing on the Chinese-English language pair in the Constrained Track. Translating literary texts poses significant challenges due to the nuanced meanings, id
Externí odkaz:
http://arxiv.org/abs/2409.16539
Autor:
Wu, Zhanglin, Luo, Yuanchang, Wei, Daimeng, Zheng, Jiawei, Wei, Bin, Li, Zongyao, Shang, Hengchao, Guo, Jiaxin, Li, Shaojun, Zhang, Weidong, Xie, Ning, Yang, Hao
This paper presents the submission of Huawei Translation Services Center (HW-TSC) to machine translation tasks of the 20th China Conference on Machine Translation (CCMT 2024). We participate in the bilingual machine translation task and multi-domain
Externí odkaz:
http://arxiv.org/abs/2409.14842
Autor:
Wu, Zhanglin, Wei, Daimeng, Li, Zongyao, Shang, Hengchao, Guo, Jiaxin, Li, Shaojun, Rao, Zhiqiang, Luo, Yuanchang, Xie, Ning, Yang, Hao
This paper presents the submission of Huawei Translate Services Center (HW-TSC) to the WMT24 general machine translation (MT) shared task, where we participate in the English to Chinese (en2zh) language pair. Similar to previous years' work, we use t
Externí odkaz:
http://arxiv.org/abs/2409.14800
Large language models can enhance automatic speech recognition systems through generative error correction. In this paper, we propose Pinyin-enhanced GEC, which leverages Pinyi, the phonetic representation of Mandarin Chinese, as supplementary inform
Externí odkaz:
http://arxiv.org/abs/2409.13262