Search Results for author: Mei Tu

Found 9 papers, 3 papers with code

Long-range Sequence Modeling with Predictable Sparse Attention

no code implementations ACL 2022 Yimeng Zhuang, Jing Zhang, Mei Tu

(2) A sparse attention matrix estimation module, which predicts dominant elements of an attention matrix based on the output of the previous hidden state cross module.

Math

Multi-Teacher Knowledge Distillation For Text Image Machine Translation

no code implementations9 May 2023 Cong Ma, Yaping Zhang, Mei Tu, Yang Zhao, Yu Zhou, Chengqing Zong

Text image machine translation (TIMT) has been widely used in various real-world applications, which translates source language texts in images into another target language sentence.

Knowledge Distillation Machine Translation +2

E2TIMT: Efficient and Effective Modal Adapter for Text Image Machine Translation

1 code implementation9 May 2023 Cong Ma, Yaping Zhang, Mei Tu, Yang Zhao, Yu Zhou, Chengqing Zong

Furthermore, the ablation studies verify the generalization of our method, where the proposed modal adapter is effective to bridge various OCR and MT models.

Machine Translation Optical Character Recognition +2

Label-Free Multi-Domain Machine Translation with Stage-wise Training

no code implementations6 May 2023 Fan Zhang, Mei Tu, Sangha Kim, Song Liu, Jinyao Yan

Our model is composed of three parts: a backbone model, a domain discriminator taking responsibility to discriminate data from different domains, and a set of experts that transfer the decoded features from generic to specific.

Machine Translation Translation

Improving End-to-End Text Image Translation From the Auxiliary Text Translation Task

1 code implementation8 Oct 2022 Cong Ma, Yaping Zhang, Mei Tu, Xu Han, Linghui Wu, Yang Zhao, Yu Zhou

End-to-end text image translation (TIT), which aims at translating the source language embedded in images to the target language, has attracted intensive attention in recent research.

Multi-Task Learning Translation

Angular Gap: Reducing the Uncertainty of Image Difficulty through Model Calibration

1 code implementation18 Jul 2022 Bohua Peng, Mobarakol Islam, Mei Tu

In this work, we propose Angular Gap, a measure of difficulty based on the difference in angular distance between feature embeddings and class-weight embeddings built by hyperspherical learning.

Unsupervised Domain Adaptation

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