8 citations · 21 across the 33 of their papers we have counts for
9 papers · 2 filters
M-Ped: Multi-Prompt Ensemble Decoding for Large Language Models
Jiaxin Guo, Daimeng Wei, Yuanchang Luo +8
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 p…
Multilingual Transfer and Domain Adaptation for Low-Resource Languages of Spain
Yuanchang Luo, Zhanglin Wu, Daimeng Wei +9
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 participa…
Exploring the traditional NMT model and Large Language Model for chat translation
Jinlong Yang, Hengchao Shang, Daimeng Wei +10
This paper describes the submissions of Huawei Translation Services Center(HW-TSC) to WMT24 chat translation shared task on EnglishGermany (en-de) bidirection. The…
Machine Translation Advancements of Low-Resource Indian Languages by Transfer Learning
Bin Wei, Jiawei Zhen, Zongyao Li +10
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 transla…
An End-to-End Speech Summarization Using Large Language Model
Hengchao Shang, Zongyao Li, Jiaxin Guo +5
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 i…
A Novel Paradigm Boosting Translation Capabilities of Large Language Models
Jiaxin Guo, Hao Yang, Zongyao Li +3
This paper presents a study on strategies to enhance the translation capabilities of large language models (LLMs) in the context of machine translation (MT) tasks. The paper propos…
DeMPT: Decoding-enhanced Multi-phase Prompt Tuning for Making LLMs Be Better Context-aware Translators
Xinglin Lyu, Junhui Li, Yanqing Zhao +4
Generally, the decoder-only large language models (LLMs) are adapted to context-aware neural machine translation (NMT) in a concatenating way, where LLMs take the concatenation of…
R-BI: Regularized Batched Inputs enhance Incremental Decoding Framework for Low-Latency Simultaneous Speech Translation
Jiaxin Guo, Zhanglin Wu, Zongyao Li +6
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-L…
UCorrect: An Unsupervised Framework for Automatic Speech Recognition Error Correction
Jiaxin Guo, Minghan Wang, Xiaosong Qiao +9
Error correction techniques have been used to refine the output sentences from automatic speech recognition (ASR) models and achieve a lower word error rate (WER). Previous works u…