Papers by Rao Fu
Simple and Effective Curriculum Pointer-Generator Networks for Reading Comprehension over Long Narratives (P19-1)
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Yi Tay, Shuohang Wang, Anh Tuan Luu, Jie Fu, Minh C. Phan, Xingdi Yuan, Jinfeng Rao, Siu Cheung Hui, Aston Zhang
| Challenge: | Using a pointer-generator framework for reading/sampling over large documents, we propose a framework for learning over long narratives where documents easily span over thousands of tokens. |
| Approach: | They propose a curriculum learning (CL) based pointer-generator framework for reading/sampling over large documents, enabling diverse training of the neural model based on the notion of alternating contextual difficulty. |
| Outcome: | The proposed framework improves on the NarrativeQA reading comprehension benchmark and reaches state-of-the-art performance. |
Lightweight and Efficient Neural Natural Language Processing with Quaternion Networks (P19-1)
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| Challenge: | Existing models for natural language processing are heavily parameterized and memory inefficient. |
| Approach: | They propose a series of lightweight and memory efficient neural architectures for NLP tasks . they propose quaternion algebra and hypercomplex spaces for computation . |
| Outcome: | The proposed models enable expressive inter-component interactions and significantly reduce parameter size without loss of performance. |
Uncertainty-Calibrated Elastic Alignment for Multimodal Sentiment Analysis with Missing Modalities (2026.findings-acl)
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Kang He, Yuzhe Ding, Rao Fu, Yukang Feng, Kaipeng Zhang, Yiming Liu, Fei Li, Chong Teng, Donghong Ji
| Challenge: | Existing methods for multimodal sentiment analysis are often dynamically incomplete. |
| Approach: | They propose a new uncertainty-calibrated elastic alignment framework to address these issues by employing probabilistic imputation to capture cross-modal ambiguity and leverage the estimated uncertainty to drive elastic alignment. |
| Outcome: | The proposed framework outperforms state-of-the-art models in multiple benchmarks and consistently outperformed existing models. |
ScratchEval: Are GPT-4o Smarter than My Child? Evaluating Large Multimodal Models with Visual Programming Challenges (2025.naacl-short)
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| Challenge: | Recent advances in large multimodal models (LMMs) have demonstrated impressive code generation capabilities, primarily evaluated through image-to-code benchmarks. |
| Approach: | They propose a visual programming reasoning benchmark based on Scratch, a block-based visual programming language widely used in children’s programming education. |
| Outcome: | The proposed framework evaluates the visual programming ability of large multimodal models by integrating visual elements and embedded programming logic. |