Papers by Xiaojun Xue
A Unified Joint Approach with Topological Context Learning and Rule Augmentation for Knowledge Graph Completion (2024.findings-acl)
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| Challenge: | Existing knowledge graph completion methods perform simple linear update on relation representation, and only local neighborhood information is aggregated, making it difficult to capture logic semantic between relations and global topological context information. |
| Approach: | They propose a joint approach with Topological Context learning and Rule Augmentation (TCRA) it uses a topological context learning mechanism and a relation rule context learning system . |
| Outcome: | The proposed approach performs better on three benchmark datasets and is widely used in knowledgeintensive applications. |
Constrained Tuple Extraction with Interaction-Aware Network (2023.acl-long)
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| Challenge: | Existing knowledge triples lack constraints for their authenticity due to spatial, temporal, or other constraints. |
| Approach: | They propose a constrained tuple extraction task to guarantee the validity of knowledge tifles by using an interaction-aware network to extract constrained text. |
| Outcome: | The proposed model outperforms existing models on the dataset and the public CaRB dataset. |
WenetSpeech-Wu: Datasets, Benchmarks, and Models for a Unified Chinese Wu Dialect Speech Processing Ecosystem (2026.findings-acl)
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Chengyou Wang, Mingchen Shao, Jingbin Hu, Zeyu Zhu, Hongfei Xue, Bingshen Mu, Xin Xu, Xingyi Duan, Binbin Zhang, Zhu Pengcheng, Chuang Ding, Xiaojun Zhang, Hui Bu, Lei Xie
| Challenge: | despite its linguistic significance, the Wu dialect of Chinese has long been hindered by the lack of large-scale speech data, standardized evaluation benchmarks, and publicly available models. |
| Approach: | They propose to use WenetSpeech-Wu as a large-scale, multi-dimensionally annotated open-source speech corpus for the Wu dialect of Chinese. |
| Outcome: | The proposed dataset includes 8,000 hours of speech data and strong open-source models . the proposed dataset is competitive and empirically validated . |