most citedSelf-Distillation Mixup Training for Non-autoregressive Neural Machine Translation

8 citations · 13 across the 3 of their papers we have counts for

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cs.CL2024

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…

cs.CL20241 cited

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…

cs.CL2024

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…

cs.CL2024

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…

cs.CL2024

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…

cs.CL20242 cited

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…