8 citations · 12 across the 7 of their papers we have counts for
7 papers · 1 filter
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…
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…
Text Style Transfer Back-Translation
Daimeng Wei, Zhanglin Wu, Hengchao Shang +6
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 transla…
Joint-training on Symbiosis Networks for Deep Nueral Machine Translation models
Zhengzhe Yu, Jiaxin Guo, Minghan Wang +11
Deep encoders have been proven to be effective in improving neural machine translation (NMT) systems, but it reaches the upper bound of translation quality when the number of encod…