Papers by Feiqi Cao
ChuLo: Chunk-Level Key Information Representation for Long Document Understanding (2025.findings-acl)
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| Challenge: | Traditional approaches to truncate inputs, sparse self-attention, and chunking often lead to information loss and hinder the model’s ability to capture long-range dependencies. |
| Approach: | They propose a novel chunk representation method that uses unsupervised keyphrase extraction to group input tokens to retain core document content while reducing input length. |
| Outcome: | The proposed method minimizes information loss and improves the efficiency of Transformer-based models. |
Understanding Attention for Vision-and-Language Tasks (2022.coling-1)
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| Challenge: | Attention mechanism has been used in Vision-and-Language (VL) tasks to bridge the semantic gap between visual and textual clues. |
| Approach: | They conduct a comprehensive analysis on understanding the role of attention alignment by looking into attention score calculation methods and checking how it represents the visual region’s and textual token’s significance for the global assessment. |
| Outcome: | The attention score calculation methods represent visual region’s and textual token’s significance for the global assessment. |