Summary of Ldp: Generalizing to Multilingual Visual Information Extraction by Language Decoupled Pretraining, By Huawen Shen et al.
LDP: Generalizing to Multilingual Visual Information Extraction by Language Decoupled Pretraining
by Huawen Shen, Gengluo Li, Jinwen Zhong, Yu Zhou
First submitted to arxiv on: 19 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Language Decoupled Pre-training (LDP) paradigm and model (LDM) enables impressive cross-lingual generalization in visual information extraction tasks. By decoupling language bias from document images, LDM leverages monolingual pre-training data to achieve state-of-the-art performance on multilingual benchmarks. This approach outperforms existing multilingual pre-trained models and maintains competitiveness on downstream monolingual/English benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers better understand documents written in different languages. Currently, most computer models are only trained on English text and don’t work well with other languages. The researchers found that by removing language-specific information from images, they can train a model to work across many languages. This new approach is called Language Decoupled Pre-training (LDP) and it works better than existing methods for processing multilingual documents. |
Keywords
» Artificial intelligence » Generalization
