Summary of Improving Chinese Character Representation with Formation Tree, by Yang Hong et al.
Improving Chinese Character Representation with Formation Tree
by Yang Hong, Yinfei Li, Xiaojun Qiao, Rui Li, Junsong Zhang
First submitted to arxiv on: 19 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 A novel deep learning model called Formation Tree-CLIP (FT-CLIP) is proposed to address the challenges of recognizing Chinese characters. The approach leverages the tree structure of character sequences to learn effective representations. Unlike prior methods that rely on radical-based sequences, FT-CLIP incorporates a dedicated tree encoder and masking techniques for efficient training. This leads to improved performance in both seen and unseen character recognition tasks. The model is evaluated extensively, demonstrating its generality and usability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Chinese characters are difficult to recognize due to their vast number and continuous growth. Previous methods tried radical-based sequences but didn’t fully use the tree structure of these sequences. A new approach called FT-CLIP (Formation Tree-CLIP) tries to fix this by using trees to represent characters and a special encoder for trees. This helps with recognizing both familiar and new characters. The model is tested and shown to be very good at recognizing Chinese characters. |
Keywords
* Artificial intelligence * Deep learning * Encoder
