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Jing Zhou

ACL 2026×2AAAI 2026SIGIR 2026推理检索与知识效率与系统可解释性评测与基准
新星分贡献者
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一作贡献30
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方向广度16
合作网络6
1 一作 · 4 篇 · ACL 2026 / AAAI 2026 / SIGIR 2026

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2026 顶会论文

4
一作ACL 2026Poster· Natural Language Processing

Capability Decomposition for Unified Information Extraction via Hierarchical Mixture-of-Experts

Jing Zhou · Peng Wang · Wenjun Ke · Jiajun Liu · Yao He

摘要

Unified Information Extraction (UIE) aims to handle heterogeneous IE tasks within a single framework, but existing methods often suffer from inconsistent schema representation, implicitly intermediate reasoning and full-parameter adaptation, which limit generalization, interpretability and parame...

效率与系统可解释性推理OpenReview 项目页
作者位次 #4/6ACL 2026Poster· Natural Language Processing

On the Role of Discriminative Models in Generative Relation Extraction

Guozheng Li · Peng Wang · Zijie Xu · Jing Zhou · Jiajun Liu · Ziyu Shang

摘要

Relation extraction (RE) identifies semantic relations between entities in text, with existing methods falling into two main paradigms: discriminative and generative. Discriminative models encode sentences and entities into relation representations and classify the most likely relation, whereas g...

可解释性评测与基准检索与知识OpenReview 项目页
作者位次 #6/9SIGIR 2026Poster· Information Retrieval (Industry)

M²GR: Generative User Interest Modeling via Multi-Granularity Multi-Objective CoT for Industrial Recommendation

Yuqi Zhang · Jingwen Shi · Wen Shi · Jia Li · Zhen Chen · Jing Zhou · Dongyue Wang · Xiwei Zhao · Sulong Xu

摘要

User interest modeling plays a vital role in industrial recommendation systems (RSs). Existing generative recommendation (GR) methods rely on single-step direct inference, which falls short of deeply modeling complex and dynamically evolving user interest. Recent chain-of-thought (CoT)-based GR m...

推理效率与系统检索与知识OpenReview

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