Papers by Yunhe Pang
RPC-Bench: A Fine-grained Benchmark for Research Paper Comprehension (2026.acl-long)
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Yelin Chen, Fanjin Zhang, Suping Sun, Yunhe Pang, Yuanchun Wang, Jian Song, XiaoYan Li, Lei Hou, Shu Zhao, Jie Tang, Juanzi Li
| Challenge: | Existing benchmarks for understanding research papers offer limited fine-grained evaluation at scale. |
| Approach: | They propose a large-scale question-answering benchmark built from review–rebuttal exchanges of high-quality computer science papers. |
| Outcome: | The proposed model is based on human-verified QA pairs and contains 15K questions. |
CoE: A Clue of Emotion Framework for Emotion Recognition in Conversations (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) are limited in interpreting complex conversational streams. |
| Approach: | They propose a Clue of Emotion framework which integrates key conversational clues to enhance the ERC task. |
| Outcome: | The proposed framework outperforms EmoryNLP, MELD, and IEMOCAP in the role-playing, speaker identification, and emotion reasoning tasks. |
HopWeaver: Cross-Document Synthesis of High-Quality and Authentic Multi-Hop Questions (2026.acl-long)
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| Challenge: | Multi-Hop Question Answering (MHQA) is a critical benchmark for evaluating the model’s ability to integrate information from diverse sources. |
| Approach: | They propose a framework that synthesizes authentic multi-hop questions without manual annotation without the need for manual guidance. |
| Outcome: | The proposed framework synthesizes bridge and comparison questions without human intervention and achieves comparable or superior quality to human-annotated datasets at a lower cost. |
CARE: A Disagreement Detection Framework with Concept Alignment and Reasoning Enhancement (2025.emnlp-main)
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| Challenge: | Existing approaches to disagreement detection are limited by conceptual gap and reasoning gap. |
| Approach: | They propose a conceptual alignment and reasoning enhancement framework to address the conceptual gap and the reasoning gap in disagreement detection. |
| Outcome: | The proposed framework shows superior performance in zero-shot and supervised learning settings, both within and across domains. |
Learnable Dependency-based Double Graph Structure for Aspect-based Sentiment Analysis (2022.coling-1)
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| Challenge: | Existing methods for aspect-based sentiment classification are susceptible to dependency tree due to noisy information and neglecting rich relation information between words. |
| Approach: | They propose a dependency-based double graph model for aspect-based sentiment classification that incorporates structure, relations and linguistic features into the sentiment text. |
| Outcome: | The proposed model is superior to state-of-the-art methods on four benchmark datasets. |