Papers by Jiong Wang
Named Entity and Relation Extraction with Multi-Modal Retrieval (2022.findings-emnlp)
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| Challenge: | Existing approaches to name entity recognition and relation extraction are knowledge-based and may not be highly relevant. |
| Approach: | They propose a multi-modal named entity recognition framework that leverages image information to improve the performance of NER and relation extraction. |
| Outcome: | The proposed framework can achieve state-of-the-art on four multi-modal named entity recognition datasets and one multi-module relation extraction dataset. |
T-MES: Trait-Aware Mix-of-Experts Representation Learning for Multi-trait Essay Scoring (2025.coling-main)
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| Challenge: | Existing methods for automatic essay scoring fail to learn trait representations and ignore correlations between trait scores. |
| Approach: | They propose a multi-trait essay scoring method based on Trait-Aware Mix-of-Experts Representation Learning. |
| Outcome: | The proposed method improves on existing methods and improves in computational efficiency. |
Evolving Agentic Workflow Driven by Human-Agent Collaboration (2026.findings-acl)
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Yuxin Liu, Jinxuan Zhang, Yuezhang Peng, Hefeng Zhou, Xiangfeng Wang, Jiong Lou, Chentao Wu, Jie LI, Jingjing Qu, Chaochao Lu
| Challenge: | Existing approaches to generate agentic workflows using large language models are limited by high manual design costs, inefficient agentic search, and poor dynamic adaptability to new tasks and human preferences. |
| Approach: | They propose an evolutionary framework for generating agentic workflows through human-agent collaboration using evolutionary algorithms that mutate and cross over their structures, prompts, and LLM backbones. |
| Outcome: | The proposed framework surpasses other automated baselines by 27.34% while achieving comparable performance to o1-preview at only one-fourth of the cost. |
Improving Prompt Generalization for Cross-prompt Essay Trait Scoring from the Scoring-invariance Perspective (2025.findings-emnlp)
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| Challenge: | Existing research on cross-prompt trait essay scoring focuses on improving model generalization by obtaining prompt-invariant representations. |
| Approach: | They propose a scoring-invariant learning objective that encourages the model to focus on intrinsic information within the essay that reflects its quality during training, thereby learning generic scoring features. |
| Outcome: | The proposed scoring-invariant learning objective encourages the model to focus on intrinsic information within the essay that reflects its quality during training, thereby learning generic scoring features. |