Papers by Yong Guan
Complex Evolutional Pattern Learning for Temporal Knowledge Graph Reasoning (2022.acl-short)
Copied to clipboard
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. |
Incorporating Syntax and Frame Semantics in Neural Network for Machine Reading Comprehension (2020.coling-main)
Copied to clipboard
| Challenge: | Existing methods for machine reading comprehension rely on manually defined features and are difficult to generalize to other tasks. |
| Approach: | They propose a Syntax and Frame Semantics model for Machine Reading Comprehension which takes full advantage of syntax and frame semantics to get richer text representation. |
| Outcome: | The proposed model outperforms ten state-of-the-art models on machine reading comprehension tasks. |
A Frame-based Sentence Representation for Machine Reading Comprehension (2020.acl-main)
Copied to clipboard
| Challenge: | Existing machine learning approaches do not have above semantic knowledge to address complicated MRC questions. |
| Approach: | They propose a frame-based Sentence Representation method which integrates frame semantic knowledge to facilitate sentence modelling. |
| Outcome: | The proposed method performs better than state-of-the-art methods on machine reading comprehension task. |
Trigger-Argument based Explanation for Event Detection (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing works on ED use words or phrases to explain models’ inner mechanisms, but for ED, the event structure is more enlightening clues to explain model behaviors. |
| Approach: | They propose a Trigger-Argument based Explanation method which can utilize event structure knowledge to uncover a faithful interpretation for existing ED models at neuron level. |
| Outcome: | The proposed method can reveal the process by which the model predicts on the large-scale MAVEN and the widely-used ACE 2005 datasets. |
MAVEN-ARG: Completing the Puzzle of All-in-One Event Understanding Dataset with Event Argument Annotation (2024.acl-long)
Copied to clipboard
Xiaozhi Wang, Hao Peng, Yong Guan, Kaisheng Zeng, Jianhui Chen, Lei Hou, Xu Han, Yankai Lin, Zhiyuan Liu, Ruobing Xie, Jie Zhou, Juanzi Li
| Challenge: | Existing datasets for event understanding have limited coverage due to complexity of tasks. |
| Approach: | They propose a dataset that augments MAVEN datasets with event argument annotations . they propose 98,591 events and 290,613 arguments obtained with laborious human annotation . |
| Outcome: | The proposed dataset is the first all-in-one dataset supporting event detection, event argument extraction, and event relation extraction. |
TacoERE: Cluster-aware Compression for Event Relation Extraction (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing work on event relation extraction focuses on modeling the entire document . existing methods cannot handle long-range dependencies and information redundancy . |
| Approach: | They propose a compression-then-extraction paradigm for event relation extraction . they propose document clustering for modeling event dependencies and then a cluster summarization method . |
| Outcome: | The proposed method simplifies and highlights important text content of clusters for mitigating redundancy and event distance. |
Integrating Semantic Scenario and Word Relations for Abstractive Sentence Summarization (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing graph-based methods only consider word relations or structure information, which neglect the correlation between them. |
| Approach: | They propose a Dual Graph network for Abstractive Sentence Summarization that captures word relations and structure information from sentences. |
| Outcome: | The proposed model outperforms state-of-the-art methods on two popular benchmark datasets. |
UERLens: Understanding Event Relations in Large Language Models (2026.acl-short)
Copied to clipboard
| Challenge: | Existing studies on event relation extraction (ERE) have focused on improving model performance. |
| Approach: | They propose an interpretability framework for understanding event relations in large language models . they first construct a counterfactual dataset that includes causal, temporal, and sub-event relations . |
| Outcome: | The proposed framework improves event relation extraction by leveraging internal features to train a lightweight classifier. |
CoreCodeBench: Decoupling Code Intelligence via Fine-Grained Repository-Level Tasks (2026.acl-long)
Copied to clipboard
Lingyue Fu, Hao Guan, Bolun Zhang, Haowei Yuan, Yaoming Zhu, Lin Qiu, ZongYu Wang, Xuezhi Cao, Xunliang Cai, Weiwen Liu, Weinan Zhang, Yong Yu
| Challenge: | Existing large language models for software engineering rely on coarse-grained pass rates obscuring specific cognitive bottlenecks. |
| Approach: | They propose a repository-level benchmark that dissects coding capabilities through atomized tasks. |
| Outcome: | The proposed framework achieves a 78.55% validity yield, surpassing the 31.7% retention rate of SWE-bench-Verified. |
Frame Semantic-Enhanced Sentence Modeling for Sentence-level Extractive Text Summarization (2021.emnlp-main)
Copied to clipboard
| Challenge: | Sentence-level extractive text summarization is difficult to model the importance of sentences. |
| Approach: | They propose a Frame Semantic-Enhanced Sentence Modeling for Extractive Summarization that leverages Frame semantics to model sentences from both intra-sentence level and inter-sentent level. |
| Outcome: | The proposed model outperforms six state-of-the-art methods on two benchmark corpus datasets. |
MMD-ERE: Multi-Agent Multi-Sided Debate for Event Relation Extraction (2025.coling-main)
Copied to clipboard
| Challenge: | Existing research indicates that LLMs can be overconfident and stubborn. |
| Approach: | They propose a multi-agent multi-sided debate approach for event relation extraction which explores the understanding of event relations between different participants before and after the debate. |
| Outcome: | The proposed approach outperforms established baselines on various ERE tasks and LLMs. |