Papers by Dapeng Wu
Revisiting Interpolation Augmentation for Speech-to-Text Generation (2024.findings-acl)
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Chen Xu, Jie Wang, Xiaoqian Liu, Qian Dong, Chunliang Zhang, Tong Xiao, JingBo Zhu, Dapeng Man, Wu Yang
| Challenge: | Existing approaches to speech-to-text generation tasks are limited by the lack of extensive labeled datasets. |
| Approach: | They propose to use interpolation augmentation to construct virtual training samples by transforming inputs and labels to enhance generalization in other domains. |
| Outcome: | The proposed approach significantly improves performance across diverse tasks, architectures, and data scales, offering a promising avenue for more robust S2T systems in resource-constrained settings. |
Learning While Staying Curious: Entropy-Preserving Supervised Fine-Tuning via Adaptive Self-Distillation for Large Reasoning Models (2026.acl-long)
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Hao Wang, Hao Gu, Hongming Piao, Kaixiong Gong, Yuxiao Ye, Xiangyu Yue, Sirui Han, Yike Guo, Dapeng Wu
| Challenge: | Recent advances establish "SFT-then-RL" as the defacto paradigm for enhancing large reasoning mod- els on automatically verifiable tasks. |
| Approach: | They propose an entropy-preserving SFT method to enhance exploration capabilities through intrinsic curiosity. |
| Outcome: | The proposed method outperforms the vanilla method on reasoning tasks by 2.5 points . it also outperformed the vanilla SFT by 2.9 points on out-of-distribution tasks . |
Tensor Product Generation Networks for Deep NLP Modeling (N18-1)
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| Challenge: | Using Tensor Product Representations (TPRs) we propose a new architecture for natural language processing based on the principle that hypothesis space for learning includes network hypotheses that are independently known to be suitable for performing the target task. |
| Approach: | They propose a Tensor Product Generation Network (TPGN) which is capable of carrying out TPR computation but uses unconstrained deep learning to design its internal representations. |
| Outcome: | The proposed architecture outperforms baselines on the COCO dataset and can interpret internal representations and operations. |
Sherry: Hardware-Efficient 1.25-Bit Ternary Quantization via Fine-grained Sparsification (2026.acl-long)
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| Challenge: | ternary quantization is a powerful solution for resource-constrained edge devices . current implementations suffer from a fundamental misalignment with commodity hardware . |
| Approach: | They propose a hardware-efficient ternary quantization framework that packs weights into five bits to restore power-of-two alignment. |
| Outcome: | The proposed framework reduces weights to -1, 0, +1 while preserving power-of-two alignment. |
A Batch Normalized Inference Network Keeps the KL Vanishing Away (2020.acl-main)
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| Challenge: | Variational Autoencoder (VAE) is widely used to approximate a model’s posterior on latent variables. |
| Approach: | They propose to let the Kullback–Leibler divergence individual follow a distribution across the whole dataset and analyze that it is sufficient to prevent posterior collapse by keeping the expectation of the KL’s distribution positive. |
| Outcome: | The proposed approach can avoid posterior collapse effectively and efficiently without introducing any new model component or modifying the objective. |
Quaff: Quantized Parameter-Efficient Fine-Tuning under Outlier Spatial Stability Hypothesis (2025.acl-long)
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| Challenge: | Existing methods for quantized fine-tuning fail to address activation outliers . existing methods incur high computational/memory costs or fail to adequately address outlier activation . |
| Approach: | They propose a Quantized parameter-efficient fine-tuning framework that suppresses outliers exclusively in invariant channels using lightweight operations. |
| Outcome: | The proposed framework reduces outliers in invariant channels while reducing quantization errors. |