Papers by Wenpeng Hu
Feature Projection for Improved Text Classification (2020.acl-main)
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| Challenge: | In sentiment classification, there are some good features that are indicative of class labels, but there are also many common features that do not discriminate for classification. |
| Approach: | They propose to project existing features into the orthogonal space of the common features and make them more discriminative for classification. |
| Outcome: | The proposed method improves CNN, RNN, Transformer, and Bert based text classification and obtains markedly better results. |
IS-CoT: Breaking the Long-form Generation Collapse via Interleaved Structural Thinking (2026.acl-long)
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Zechen Sun, Yuyang Sun, Zecheng Tang, Juntao Li, Wenpeng Hu, Wenliang Chen, Zhunchen Luo, Guotong Geng, Min Zhang
| Challenge: | Existing models with reasoning capabilities suffer from a severe length collapse in open-ended writing . |
| Approach: | They propose a framework that embeds a dynamic plan-write-reflect cycle into the generation process and train a model with interleaved reasoning traces. |
| Outcome: | The proposed framework achieves state-of-the-art performance on long-form benchmarks compared to other models on the same dataset. |
One Time of Interaction May Not Be Enough: Go Deep with an Interaction-over-Interaction Network for Response Selection in Dialogues (P19-1)
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| Challenge: | Currently, retrieval-based dialogues are performed in shallow ways . a recent study investigated the problem of context-response matching in open-domain . |
| Approach: | They propose a model that lets utterance-response interaction go deep by stacking interaction blocks. |
| Outcome: | The proposed model outperforms state-of-the-art methods on three benchmark data sets. |
Who Is Speaking to Whom? Learning to Identify Utterance Addressee in Multi-Party Conversations (D19-1)
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| Challenge: | In multi-party conversations, addressee information is not always explicit . researchers have spent great efforts to understand conversations between two participants, which is known as multi-part conversation. |
| Approach: | They propose a who-to-whom model which models users and utterances in a conversation session jointly in an interactive way. |
| Outcome: | The proposed model outperforms baseline models on the Ubuntu Multi-Party Conversation Corpus and shows consistent improvements. |
Transformation of Dense and Sparse Text Representations (2020.coling-main)
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| Challenge: | Existing approaches to NLP to leverage sparsity have been limited due to the gap with dense representations. |
| Approach: | They propose a Semantic Transformation method to bridge dense and sparse spaces and propose supervised NLP tasks to use both spaces. |
| Outcome: | Experiments with classification tasks and natural language inference tasks show that the proposed method is effective. |
Unveiling the Potential of BERT-family: A New Recipe for Building Scalable, General and Competitive Large Language Models (2025.acl-long)
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| Challenge: | Generative large language models (LLMs) have significantly influenced various aspects of society, reshaping how we access and interact with information and knowledge. |
| Approach: | They propose a pre-training task that helps BERT-family excel in wider applications . they also explore the integration of cutting-edge technologies into their models to further enhance their capabilities. |
| Outcome: | The proposed model exhibits performance levels comparable to current SOTA LLMs across a spectrum of tasks. |
Translation vs. Dialogue: A Comparative Analysis of Sequence-to-Sequence Modeling (2020.coling-main)
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| Challenge: | Existing models for machine translation and dialogue response generation require a large number of handcrafted features. |
| Approach: | They propose to interpret a general neural model comparatively by using the seq2seq model in two mainstream NLP tasks. |
| Outcome: | The proposed model is used in two mainstream NLP tasks and is compared with a standard model. |
When Rules Learn: A Self-Evolving Agent for Legal Case Retrieval (2026.findings-acl)
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| Challenge: | Existing dense retrieval methods have achieved notable progress, but their effectiveness in legal case retrieval remains limited. |
| Approach: | They propose a self-evolving framework for rule-driven query rewriting that enhances BM25 without any parameter training. |
| Outcome: | The proposed framework outperforms non-evolutionary baselines, including human-designed rules and greedy rule selection, especially when powered by a high-capacity core LLM. |
Using the Past Knowledge to Improve Sentiment Classification (2020.findings-emnlp)
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| Challenge: | Existing model retains knowledge learned from past tasks and selectively transfers it to new task to help it learn better. |
| Approach: | They propose a lifelong learning model that can retain and selectively transfer the knowledge learned in the past to help learn the new task. |
| Outcome: | The proposed model outperforms strong baselines, including even multiple task learning. |
Modeling Personalization in Continuous Space for Response Generation via Augmented Wasserstein Autoencoders (D19-1)
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| Challenge: | Existing work on variableal autoencoders and waterstein autoencoding models has shown significant progress in open-domain response generation. |
| Approach: | They propose to embed user-level and utterance-level information into two multimodal distributions and combine them into a mixed distribution. |
| Outcome: | The proposed model outperforms state-of-the-art models on a large-scale real-world dataset. |
MEraser: An Effective Fingerprint Erasure Approach for Large Language Models (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) have raised critical concerns about model ownership and intellectual property protection. |
| Approach: | They propose a method for effectively removing backdoor-based fingerprints from LLMs . they propose deleting backdoor fingerprints using a transferable erasure mechanism . |
| Outcome: | The proposed method removes backdoor-based fingerprints while maintaining model performance. |
Improved Training of Deep Text Clustering (2023.findings-emnlp)
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| Challenge: | Existing methods for deep clustering optimization with shallow models have limited performance due to poor power of feature learning. |
| Approach: | They propose a general deep clustering optimization method that leverages information feedback to construct generalized labels to optimize the deep model. |
| Outcome: | The proposed method reduces the impact of noise on the clustering process by using correlation relationship between the samples. |