Papers by Xuying Meng
CofeNet: Context and Former-Label Enhanced Net for Complicated Quotation Extraction (2022.coling-1)
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| Challenge: | Existing solutions for quotation extraction use rule-based approaches and sequence labeling models. |
| Approach: | They propose a Context and Former-Label Enhanced Net for quotation extraction. |
| Outcome: | The proposed method achieves state-of-the-art performance on complicated quotation extraction on two public datasets and one proprietary dataset. |
CORT: A New Baseline for Comparative Opinion Classification by Dual Prompts (2022.findings-emnlp)
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| Challenge: | Comparative opinion classification is a common linguistic phenomenon. |
| Approach: | They propose a framework for comparative opinion classification using embedded knowledge in pre-trained language models. |
| Outcome: | The proposed framework delivers state-of-the-art and robust performance on all benchmark datasets. |
A Dual-Channel Framework for Sarcasm Recognition by Detecting Sentiment Conflict (2022.findings-naacl)
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| Challenge: | Sarcasm employs ambivalence, where one says something positive but actually means negative . linguistically, it is difficult to recognize such sentiment conflict because the sentiments are mixed or even implicit . |
| Approach: | They propose a Dual-Channel Framework to model literal and implied sentiments separately . they propose sarcastic networks that can detect sarcasm sentiments in political debates . |
| Outcome: | The proposed framework achieves state-of-the-art on political debates and Twitter datasets. |
Graph-Assisted Large Language Models: A Perspective on Mitigating Intrinsic Limitations (2026.findings-acl)
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Haitong Luo, Fali Wang, Weiyao Zhang, Xianren Zhang, Zhiwei Zhang, Tianxiang Zhao, Minhua Lin, Jiahao Zhang, Hui Liu, Xianfeng Tang, Qi He, Suhang Wang, Xuying Meng, Yujun Zhang
| Challenge: | Large language models exhibit intrinsic limitations such as knowledge cutoff, single-threaded reasoning that hinders finer-grained branch and aggregation, and rigid collaboration mechanisms that struggle to coordinate specialized capabilities. |
| Approach: | They propose a taxonomy spanning *Graph-Assisted Knowledge Augmentation*, *Graph Assisted Reasoning and Planning*, and *Graphed LLM Collaboration*. |
| Outcome: | The proposed models show that graphs can augment and correct LLMs and support dynamic coordination among experts and agents in collaborative settings. |