Papers by Biyi Fang
TrInk: Ink Generation with Transformer Network (2025.emnlp-main)
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Zezhong Jin, Shubhang Desai, Xu Chen, Biyi Fang, Zhuoyi Huang, Zhe Li, Chong-Xin Gan, Xiao Tu, Man-Wai Mak, Yan Lu, Shujie Liu
| Challenge: | Existing methods for handwriting generation capture global dependencies and can generate high-quality handwritten samples. |
| Approach: | They propose a Transformer-based model for ink generation, TrInk, which captures global dependencies. |
| Outcome: | The proposed model reduces character error rate and word error rate by 35.56% on the IAM-OnDB dataset compared to previous models. |
SLATE: A Sequence Labeling Approach for Task Extraction from Free-form Inked Content (2022.emnlp-industry)
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Apurva Gandhi, Ryan Serrao, Biyi Fang, Gilbert Antonius, Jenna Hong, Tra My Nguyen, Sheng Yi, Ehi Nosakhare, Irene Shaffer, Soundararajan Srinivasan
| Challenge: | SLATE is a sequence labeling approach for extracting tasks from free-form content . past approaches for task extraction from typed content focus on building separate sentence-level task classification models. |
| Approach: | They propose a sequence labeling approach for extracting tasks from free-form content . they use a single, low-latency sequence labelling approach to perform sentence segmentation and classification . |
| Outcome: | The proposed model outperforms a baseline model and achieves 84.4% task F1 score and 88.4% boundary similarity score. |