Zobrazeno 1 - 10
of 202
pro vyhledávání: '"LI Zongyao"'
Autor:
Guo, Jiaxin, Wei, Daimeng, Luo, Yuanchang, Tao, Shimin, Shang, Hengchao, Li, Zongyao, Li, Shaojun, Yang, Jinlong, Wu, Zhanglin, Rao, Zhiqiang, Yang, Hao
With the widespread application of Large Language Models (LLMs) in the field of Natural Language Processing (NLP), enhancing their performance has become a research hotspot. This paper presents a novel multi-prompt ensemble decoding approach designed
Externí odkaz:
http://arxiv.org/abs/2412.18299
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, Shang, Hengchao, Wei, Daimeng, Guo, Jiaxin, Li, Zongyao, He, Xianghui, Zhang, Min, Yang, Hao
Recent advancements in integrating speech information into large language models (LLMs) have significantly improved automatic speech recognition (ASR) accuracy. However, existing methods often constrained by the capabilities of the speech encoders un
Externí odkaz:
http://arxiv.org/abs/2409.08597
Autor:
Shang, Hengchao, Li, Zongyao, Guo, Jiaxin, Li, Shaojun, Rao, Zhiqiang, Luo, Yuanchang, Wei, Daimeng, Yang, Hao
Abstractive Speech Summarization (SSum) aims to generate human-like text summaries from spoken content. It encounters difficulties in handling long speech input and capturing the intricate cross-modal mapping between long speech inputs and short text
Externí odkaz:
http://arxiv.org/abs/2407.02005
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