Papers by Xianchao Zhang
Hawkes based Representation Learning for Reasoning over Scale-free Community-structured Temporal Knowledge Graphs (2025.coling-main)
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| Challenge: | Temporal knowledge graph reasoning is a useful tool for many practical tasks. |
| Approach: | They propose a Hawkes process-based Evolutional Representation Learning Network model which learns structural information and evolutional patterns of a TKG simultaneously. |
| Outcome: | The proposed model learns structural information and evolutional patterns of a TKG simultaneously, considering the characteristics of real-world networks: community structure, scale-free and temporal decaying. |
RENN: A Rule Embedding Enhanced Neural Network Framework for Temporal Knowledge Graph Completion (2024.lrec-main)
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| Challenge: | Existing methods for temporal knowledge graph embedding do not account for structural dependencies between relations. |
| Approach: | They propose a framework that enhances temporal knowledge graph completion through rule embedding. |
| Outcome: | The proposed framework improves temporal knowledge graph completion through rule embedding. |
Unveiling Opinion Evolution via Prompting and Diffusion for Short Video Fake News Detection (2024.findings-acl)
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| Challenge: | Existing methods for short video fake news detection ignore the implicit opinions and evolving nature of opinions across modalities. |
| Approach: | They propose a short video fake news model that mines implicit opinions within short videos and promotes the evolution of both explicit and implicit opinions across all modalities. |
| Outcome: | The proposed model outperforms existing methods on a publicly available dataset for short video fake news detection. |
An Explicit-Joint and Supervised-Contrastive Learning Framework for Few-Shot Intent Classification and Slot Filling (2021.findings-emnlp)
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| Challenge: | Intent classification and slot filling are key building blocks in task-oriented dialogue systems. |
| Approach: | They propose an explicit-joint and supervised-contrastive learning framework for few-shot intent classification and slot filling. |
| Outcome: | The proposed model extracts intent and slot representations via bidirectional interactions and extends prototypical network to achieve explicit-joint learning. |
Revisiting Cross-Lingual Summarization: A Corpus-based Study and A New Benchmark with Improved Annotation (2023.acl-long)
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Yulong Chen, Huajian Zhang, Yijie Zhou, Xuefeng Bai, Yueguan Wang, Ming Zhong, Jianhao Yan, Yafu Li, Judy Li, Xianchao Zhu, Yue Zhang
| Challenge: | Existing work on cross-lingual summarization (CLS) does not consider crosslingual sources for summarizing. |
| Approach: | They propose a cross-lingual conversation summarization benchmark that explicitly considers source context. |
| Outcome: | The proposed method surpasses baselines on ConvSumX and 3 widely-used manual annotations. |