Papers by Saiping Guan
Complex Evolutional Pattern Learning for Temporal Knowledge Graph Reasoning (2022.acl-short)
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Zixuan Li, Saiping Guan, Xiaolong Jin, Weihua Peng, Yajuan Lyu, Yong Zhu, Long Bai, Wei Li, Jiafeng Guo, Xueqi Cheng
| Challenge: | Existing models for TKG reasoning focus on modeling fact sequences of a fixed length, which cannot discover complex evolutional patterns that vary in length. |
| Approach: | They propose to use a length-aware Convolutional Neural Network to handle evolutional patterns of different lengths via an easy-to-difficult curriculum learning strategy. |
| Outcome: | The proposed model improves performance under both offline and online learning strategies. |
Lost in Decomposition: Analyzing and Mitigating the Limitations of Long Context Methods via Context Dependency (2026.findings-acl)
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| Challenge: | Existing workflow-based long context methods do not perform well on specific datasets . performance degradation is associated with the indiscriminate application of long context models . |
| Approach: | They propose a training-free adaptive routing strategy to improve long context large language models' robustness. |
| Outcome: | The proposed method can be generalized to all types of datasets, but performance degradation is a concern. |
Search from History and Reason for Future: Two-stage Reasoning on Temporal Knowledge Graphs (2021.acl-long)
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| Challenge: | Temporal Knowledge Graphs (TKGs) are used in many different areas of research. |
| Approach: | They propose to use a beam search policy to induce multiple clues from historical facts . they propose to adopt a graph convolution network based sequence method to deduce answers from clues . |
| Outcome: | The proposed model can predict future facts in two stages, Clue Searching and Temporal Reasoning. |
Temporal Knowledge Graph Reasoning Based on N-tuple Modeling (2023.findings-emnlp)
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| Challenge: | Existing Temporal Knowledge Graphs (TKGs) only contain their core entities and form them as quadruples. |
| Approach: | They propose to describe a temporal fact more accurately as an n-tuple . they propose to use a neural network to learn evolutional representations of entities . |
| Outcome: | The proposed model oversimplifies and causes information loss on two datasets. |
RouteRAG: Efficient Retrieval-Augmented Generation from Text and Graph via Reinforcement Learning (2026.findings-acl)
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| Challenge: | Existing graph-based or hybrid systems lack the ability to integrate supplementary evidence as reasoning unfolds. |
| Approach: | They propose a framework that integrates non-parametric knowledge into Large Language Models . they use a RL-based framework to optimize the entire generation process via RL . |
| Outcome: | The proposed framework outperforms existing RAG frameworks in five question answering benchmarks. |
NeuInfer: Knowledge Inference on N-ary Facts (2020.acl-main)
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| Challenge: | Existing studies on knowledge inference on binary facts have focused on finding out connotative valid facts. |
| Approach: | They propose a neural network model, NeuInfer, for knowledge inference on n-ary facts. |
| Outcome: | The proposed model can cope with the task to infer an unknown element in a whole fact, while ignoring the binary facts. |
HiSMatch: Historical Structure Matching based Temporal Knowledge Graph Reasoning (2022.findings-emnlp)
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Zixuan Li, Zhongni Hou, Saiping Guan, Xiaolong Jin, Weihua Peng, Long Bai, Yajuan Lyu, Wei Li, Jiafeng Guo, Xueqi Cheng
| Challenge: | Temporal Knowledge Graphs (TKGs) store facts as triples in the form of subject, relation, object, timestamps. |
| Approach: | They propose a Temporal Knowledge Graph (TKG) model that extends each triple with a timestamp to describe dynamic facts. |
| Outcome: | The proposed model improves on six benchmark datasets with up to 5.6% performance improvement compared to the state-of-the-art models. |
Event Coreference Resolution with their Paraphrases and Argument-aware Embeddings (2020.coling-main)
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| Challenge: | Existing methods for event coreference resolution do not identify paraphrase relations between events. |
| Approach: | They propose a new event-specific paraphrase and argument-aware semantic Embedding model for event coreference resolution based on event-related paraphrases and argument embeddings . EPASE recognizes deep paraphrase relations in an event- specific context of sentences and can cover event paraphrase of more situations . |
| Outcome: | Experiments on within- and cross-document event coreference show it is superior compared to existing methods. |
Few-shot Link Prediction on Hyper-relational Facts (2024.lrec-main)
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| Challenge: | Existing methods to predict missing elements in hyper-relational facts require high-quality data. |
| Approach: | They propose a task to predict a missing entity in a hyper-relational fact with limited support instances. |
| Outcome: | The proposed model outperforms existing models on three datasets. |
Nested Event Extraction upon Pivot Element Recognition (2024.lrec-main)
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Weicheng Ren, Zixuan Li, Xiaolong Jin, Long Bai, Miao Su, Yantao Liu, Saiping Guan, Jiafeng Guo, Xueqi Cheng
| Challenge: | Nested Event Extraction (NEE) aims to extract complex event structures where an event contains other events as its arguments recursively. |
| Approach: | They propose a new model that extracts nested events mainly based on recognizing PEs. |
| Outcome: | The proposed model can extract nested events based on recognizing PEs . it incorporates information from both event types and argument roles to improve performance . |
Semantic Structure Enhanced Event Causality Identification (2023.acl-long)
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| Challenge: | Existing methods for Event Causality Identification (ECI) capture implicit associations between events, which are difficult because they lack the ability to understand the associations between two events. |
| Approach: | They propose a model that captures the implicit associations between two events and integrates the event-centric structure information into a GNN-based event aggregator. |
| Outcome: | The proposed model improves on three widely used datasets showing that it integrates event-centric and event-associated semantic elements and captures event associations. |
Integrating Deep Event-Level and Script-Level Information for Script Event Prediction (2021.emnlp-main)
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| Challenge: | Existing studies only consider a single event sequence corresponding to one common protagonist. |
| Approach: | They propose a Transformer-based model which integrates deep event-level and script-level information for script event prediction. |
| Outcome: | The proposed model is superior to existing models on the New York Times corpus . it utilizes rich information in the text to obtain more comprehensive representations . |
Inductive Link Prediction in N-ary Knowledge Graphs (2025.coling-main)
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| Challenge: | Existing methods to predict missing elements in NKGs are fixed and therefore cannot be used in real-world situations. |
| Approach: | They propose a task to predict missing elements in unseen facts involving unseent entities and roles in emerging NKGs by embedding unseense entities and role-encoding neural networks. |
| Outcome: | The proposed task outperforms representative models across all datasets. |
MetaSLRCL: A Self-Adaptive Learning Rate and Curriculum Learning Based Framework for Few-Shot Text Classification (2022.coling-1)
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| Challenge: | Existing few-shot text classification methods lack labeled data in many scenarios. |
| Approach: | They propose a meta learning framework that obtains different learning rates for different tasks and neural network layers to enable the meta learner to quickly adapt to new training data. |
| Outcome: | The proposed framework can obtain different learning rates for different tasks and neural network layers so as to enable the meta learner to quickly adapt to new tasks. |
A Survey of Link Prediction in N-ary Knowledge Graphs (2025.emnlp-main)
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Jiyao Wei, Saiping Guan, Da Li, Zhongni Hou, Miao Su, Yucan Guo, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng
| Challenge: | N-ary Knowledge Graphs (NKGs) capture n-ary facts containing more than two entities. |
| Approach: | They present the first comprehensive survey of link prediction in NKGs . they provide an overview of the field and analyze their performance and application scenarios . |
| Outcome: | The proposed methods provide an overview of the field and analyze performance and application scenarios. |