8 citations · 15 across the 5 of their papers we have counts for
5 papers
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…
Diformer: Directional Transformer for Neural Machine Translation
Minghan Wang, Jiaxin Guo, Yuxia Wang +8
Autoregressive (AR) and Non-autoregressive (NAR) models have their own superiority on the performance and latency, combining them into one model may take advantage of both. Current…
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…
Self-Distillation Mixup Training for Non-autoregressive Neural Machine Translation
Jiaxin Guo, Minghan Wang, Daimeng Wei +11
Recently, non-autoregressive (NAT) models predict outputs in parallel, achieving substantial improvements in generation speed compared to autoregressive (AT) models. While performi…