647 citations · 1.1k across the 29 of their papers we have counts for
29 papers
Context-aware Event Forecasting via Graph Disentanglement
Yunshan Ma, Chenchen Ye, Zijian Wu +3
Event forecasting has been a demanding and challenging task throughout the entire human history. It plays a pivotal role in crisis alarming and disaster prevention in various aspec…
Constructing Holistic Spatio-Temporal Scene Graph for Video Semantic Role Labeling
Yu Zhao, Hao Fei, Yixin Cao +5
Video Semantic Role Labeling (VidSRL) aims to detect the salient events from given videos, by recognizing the predict-argument event structures and the interrelationships between e…
Exploring & Exploiting High-Order Graph Structure for Sparse Knowledge Graph Completion
Tao He, Ming Liu, Yixin Cao +4
Sparse knowledge graph (KG) scenarios pose a challenge for previous Knowledge Graph Completion (KGC) methods, that is, the completion performance decreases rapidly with the increas…
Benchmarking Foundation Models with Language-Model-as-an-Examiner
Yushi Bai, Jiahao Ying, Yixin Cao +10
Numerous benchmarks have been established to assess the performance of foundation models on open-ended question answering, which serves as a comprehensive test of a model's ability…
Information Screening whilst Exploiting! Multimodal Relation Extraction with Feature Denoising and Multimodal Topic Modeling
Shengqiong Wu, Hao Fei, Yixin Cao +2
Existing research on multimodal relation extraction (MRE) faces two co-existing challenges, internal-information over-utilization and external-information under-exploitation. To co…
Few-shot Event Detection: An Empirical Study and a Unified View
Yubo Ma, Zehao Wang, Yixin Cao +1
Few-shot event detection (ED) has been widely studied, while this brings noticeable discrepancies, e.g., various motivations, tasks, and experimental settings, that hinder the unde…
Take a Break in the Middle: Investigating Subgoals towards Hierarchical Script Generation
Xinze Li, Yixin Cao, Muhao Chen +1
Goal-oriented Script Generation is a new task of generating a list of steps that can fulfill the given goal. In this paper, we propose to extend the task from the perspective of co…
Examining Inter-Consistency of Large Language Models Collaboration: An In-depth Analysis via Debate
Kai Xiong, Xiao Ding, Yixin Cao +2
Large Language Models (LLMs) have shown impressive capabilities in various applications, but they still face various inconsistency issues. Existing works primarily focus on the inc…
Large Language Model Is Not a Good Few-shot Information Extractor, but a Good Reranker for Hard Samples!
Yubo Ma, Yixin Cao, YongChing Hong +1
Large Language Models (LLMs) have made remarkable strides in various tasks. Whether LLMs are competitive few-shot solvers for information extraction (IE) tasks, however, remains an…
TGDM: Target Guided Dynamic Mixup for Cross-Domain Few-Shot Learning
Linhai Zhuo, Yuqian Fu, Jingjing Chen +2
Given sufficient training data on the source domain, cross-domain few-shot learning (CD-FSL) aims at recognizing new classes with a small number of labeled examples on the target d…
RF: A General Retrieval, Reading and Fusion Framework for Document-level Natural Language Inference
Hao Wang, Yixin Cao, Yangguang Li +3
Document-level natural language inference (DOCNLI) is a new challenging task in natural language processing, aiming at judging the entailment relationship between a pair of hypothe…
Explainable Sparse Knowledge Graph Completion via High-order Graph Reasoning Network
Weijian Chen, Yixin Cao, Fuli Feng +2
Knowledge Graphs (KGs) are becoming increasingly essential infrastructures in many applications while suffering from incompleteness issues. The KG completion task (KGC) automatical…
ERGO: Event Relational Graph Transformer for Document-level Event Causality Identification
Meiqi Chen, Yixin Cao, Kunquan Deng +4
Document-level Event Causality Identification (DECI) aims to identify causal relations between event pairs in a document. It poses a great challenge of across-sentence reasoning wi…
Prompt for Extraction? PAIE: Prompting Argument Interaction for Event Argument Extraction
Yubo Ma, Zehao Wang, Yixin Cao +4
In this paper, we propose an effective yet efficient model PAIE for both sentence-level and document-level Event Argument Extraction (EAE), which also generalizes well when there i…
What Makes the Story Forward? Inferring Commonsense Explanations as Prompts for Future Event Generation
Li Lin, Yixin Cao, Lifu Huang +4
Prediction over event sequences is critical for many real-world applications in Information Retrieval and Natural Language Processing. Future Event Generation (FEG) is a challengin…
Interactive Contrastive Learning for Self-supervised Entity Alignment
Kaisheng Zeng, Zhenhao Dong, Lei Hou +6
Self-supervised entity alignment (EA) aims to link equivalent entities across different knowledge graphs (KGs) without seed alignments. The current SOTA self-supervised EA method d…
Is Multi-Hop Reasoning Really Explainable? Towards Benchmarking Reasoning Interpretability
Xin Lv, Yixin Cao, Lei Hou +4
Multi-hop reasoning has been widely studied in recent years to obtain more interpretable link prediction. However, we find in experiments that many paths given by these models are…
Learning Relation Prototype from Unlabeled Texts for Long-tail Relation Extraction
Yixin Cao, Jun Kuang, Ming Gao +3
Relation Extraction (RE) is a vital step to complete Knowledge Graph (KG) by extracting entity relations from texts.However, it usually suffers from the long-tail issue. The traini…
Exploring and Evaluating Attributes, Values, and Structures for Entity Alignment
Zhiyuan Liu, Yixin Cao, Liangming Pan +2
Entity alignment (EA) aims at building a unified Knowledge Graph (KG) of rich content by linking the equivalent entities from various KGs. GNN-based EA methods present promising pe…
Tree-Augmented Cross-Modal Encoding for Complex-Query Video Retrieval
Xun Yang, Jianfeng Dong, Yixin Cao +3
The rapid growth of user-generated videos on the Internet has intensified the need for text-based video retrieval systems. Traditional methods mainly favor the concept-based paradi…
Multi-Channel Graph Neural Network for Entity Alignment
Yixin Cao, Zhiyuan Liu, Chengjiang Li +2
Entity alignment typically suffers from the issues of structural heterogeneity and limited seed alignments. In this paper, we propose a novel Multi-channel Graph Neural Network mod…
Low-Resource Name Tagging Learned with Weakly Labeled Data
Yixin Cao, Zikun Hu, Tat-Seng Chua +2
Name tagging in low-resource languages or domains suffers from inadequate training data. Existing work heavily relies on additional information, while leaving those noisy annotatio…
Who, Where, and What to Wear? Extracting Fashion Knowledge from Social Media
Yunshan Ma, Xun Yang, Lizi Liao +2
Fashion knowledge helps people to dress properly and addresses not only physiological needs of users, but also the demands of social activities and conventions. It usually involves…
Improving Neural Relation Extraction with Implicit Mutual Relations
Jun Kuang, Yixin Cao, Jianbing Zheng +3
Relation extraction (RE) aims at extracting the relation between two entities from the text corpora. It is a crucial task for Knowledge Graph (KG) construction. Most existing metho…