Zobrazeno 1 - 10
of 56
pro vyhledávání: '"Wu, Zhanglin"'
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
Autor:
Li, Shaojun, Wei, Daimeng, Shang, Hengchao, Guo, Jiaxin, Li, ZongYao, Wu, Zhanglin, Rao, Zhiqiang, Luo, Yuanchang, He, Xianghui, Yang, Hao
Despite recent improvements in End-to-End Automatic Speech Recognition (E2E ASR) systems, the performance can degrade due to vocal characteristic mismatches between training and testing data, particularly with limited target speaker adaptation data.
Externí odkaz:
http://arxiv.org/abs/2406.04791
Autor:
Guo, Jiaxin, Wu, Zhanglin, Li, Zongyao, Shang, Hengchao, Wei, Daimeng, Chen, Xiaoyu, Rao, Zhiqiang, Li, Shaojun, Yang, Hao
Incremental Decoding is an effective framework that enables the use of an offline model in a simultaneous setting without modifying the original model, making it suitable for Low-Latency Simultaneous Speech Translation. However, this framework may in
Externí odkaz:
http://arxiv.org/abs/2401.05700
Autor:
Wei, Daimeng, Wu, Zhanglin, Shang, Hengchao, Li, Zongyao, Wang, Minghan, Guo, Jiaxin, Chen, Xiaoyu, Yu, Zhengzhe, Yang, Hao
Back Translation (BT) is widely used in the field of machine translation, as it has been proved effective for enhancing translation quality. However, BT mainly improves the translation of inputs that share a similar style (to be more specific, transl
Externí odkaz:
http://arxiv.org/abs/2306.01318
BERTScore is an effective and robust automatic metric for referencebased machine translation evaluation. In this paper, we incorporate multilingual knowledge graph into BERTScore and propose a metric named KG-BERTScore, which linearly combines the re
Externí odkaz:
http://arxiv.org/abs/2301.12699