Papers by Xiyang Zhang
Improving Data Annotation for Low-Resource Relation Extraction with Logical Rule-Augmented Collaborative Language Models (2025.naacl-long)
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| Challenge: | Low-resource relation extraction aims to identify semantic relationships using scarce labeled data. |
| Approach: | They propose a framework that iteratively integrates high-confidence predictions of rule-enhanced relation extractors with varying scales to obtain reliable pseudo annotations from massive unlabeled samples without human supervision. |
| Outcome: | The proposed framework achieves state-of-the-art on benchmark datasets in few-shot scenarios. |
Salience-Aware Event Chain Modeling for Narrative Understanding (2021.emnlp-main)
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| Challenge: | Storytelling is the communication of interesting and related events that form a concrete process. |
| Approach: | They propose methods for extracting the principal chain from natural language text . they filter away non-salient events and supportive sentences to isolate them . authors propose novel methods for predicting and answering events from text based on event-based temporal question answering . |
| Outcome: | The proposed method improves narrative prediction and event-based temporal question answering tasks. |
CaDRL: Document-level Relation Extraction via Context-aware Differentiable Rule Learning (2025.coling-main)
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Kunli Zhang, Pengcheng Wu, Bohan Yu, Kejun Wu, Aoze Zheng, Xiyang Huang, Chenkang Zhu, Min Peng, Hongying Zan, Yu Song
| Challenge: | Existing methods for document-level relation extraction (DocRE) lack logic and transparency. |
| Approach: | They propose a Context-aware differentiable rule learning framework that learns the doc-specific logical rule to avoid suboptimal constraints. |
| Outcome: | The proposed framework outperforms existing rule-based frameworks on three DocRE datasets. |
CmEAA: Cross-modal Enhancement and Alignment Adapter for Radiology Report Generation (2025.coling-main)
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| Challenge: | Existing methods for automatic radiology report generation suffer from data bias. |
| Approach: | They propose a method that connects a vision encoder with a frozen large language model by using a cross-modal enhancement and alignment adapter. |
| Outcome: | The proposed model outperforms existing state-of-the-art methods on IU X-Ray and MIMIC-CXR datasets. |