no code implementations • 7 Mar 2024 • Qian Li, Shu Guo, Yinjia Chen, Cheng Ji, Jiawei Sheng, JianXin Li
Uncertainty representation is first designed for estimating the uncertainty scope of the entity pairs after transferring feature representations into a Gaussian distribution.
no code implementations • 23 Jan 2024 • XiaoDong Li, Jiawei Sheng, Jiangxia Cao, Wenyuan Zhang, Quangang Li, Tingwen Liu
Cross-domain recommendation (CDR) has been proven as a promising way to tackle the user cold-start problem, which aims to make recommendations for users in the target domain by transferring the user preference derived from the source domain.
no code implementations • 4 Aug 2023 • Shiyao Cui, Xin Cong, Jiawei Sheng, Xuebin Wang, Tingwen Liu, Jinqiao Shi
In this paper, we regard public pre-trained language models as knowledge bases and automatically mine the script-related knowledge via prompt-learning.
no code implementations • 20 Apr 2023 • Gehang Zhang, Bowen Yu, Jiangxia Cao, Xinghua Zhang, Jiawei Sheng, Chuan Zhou, Tingwen Liu
Graph contrastive learning (GCL) has recently achieved substantial advancements.
1 code implementation • 8 Apr 2023 • Jiangxia Cao, Xin Cong, Jiawei Sheng, Tingwen Liu, Bin Wang
Cross-Domain Sequential Recommendation (CDSR) aims to predict future interactions based on user's historical sequential interactions from multiple domains.
no code implementations • 5 Apr 2023 • Shiyao Cui, Jiangxia Cao, Xin Cong, Jiawei Sheng, Quangang Li, Tingwen Liu, Jinqiao Shi
For the first issue, a refinement-regularizer probes the information-bottleneck principle to balance the predictive evidence and noisy information, yielding expressive representations for prediction.
no code implementations • 4 Apr 2023 • Qian Li, Shu Guo, Yangyifei Luo, Cheng Ji, Lihong Wang, Jiawei Sheng, JianXin Li
In this paper, we propose a novel attribute-consistent knowledge graph representation learning framework for MMEA (ACK-MMEA) to compensate the contextual gaps through incorporating consistent alignment knowledge.
1 code implementation • COLING 2022 • Shiyao Cui, Jiawei Sheng, Xin Cong, Quangang Li, Tingwen Liu, Jinqiao Shi
Event Causality Identification (ECI), which aims to detect whether a causality relation exists between two given textual events, is an important task for event causality understanding.
no code implementations • 15 Nov 2022 • Qian Li, JianXin Li, Lihong Wang, Cheng Ji, Yiming Hei, Jiawei Sheng, Qingyun Sun, Shan Xue, Pengtao Xie
To address the above issues, we propose a Multi-Channel graph neural network utilizing Type information for Event Detection in power systems, named MC-TED, leveraging a semantic channel and a topological channel to enrich information interaction from short texts.
1 code implementation • SIGIR 2022 • Xin Cong, Jiawei Sheng, Shiyao Cui, Bowen Yu, Tingwen Liu, Bin Wang
To instantiate this strategy, we further propose a model, RelATE, which builds a dual-level attention to aggregate relationrelevant information to detect the relation occurrence and utilizes the annotated samples of the detected relations to extract the corresponding head/tail entities.
1 code implementation • 31 Mar 2022 • Jiangxia Cao, Jiawei Sheng, Xin Cong, Tingwen Liu, Bin Wang
As a promising way, Cross-Domain Recommendation (CDR) has attracted a surge of interest, which aims to transfer the user preferences observed in the source domain to make recommendations in the target domain.
1 code implementation • EMNLP 2021 • Xinghua Zhang, Bowen Yu, Tingwen Liu, Zhenyu Zhang, Jiawei Sheng, Mengge Xue, Hongbo Xu
Distantly supervised named entity recognition (DS-NER) efficiently reduces labor costs but meanwhile intrinsically suffers from the label noise due to the strong assumption of distant supervision.
no code implementations • 23 Aug 2021 • Qian Li, Shu Guo, Jia Wu, JianXin Li, Jiawei Sheng, Lihong Wang, Xiaohan Dong, Hao Peng
It ignores meaningful associations among event types and argument roles, leading to relatively poor performance for less frequent types/roles.
no code implementations • 5 Jul 2021 • Qian Li, JianXin Li, Jiawei Sheng, Shiyao Cui, Jia Wu, Yiming Hei, Hao Peng, Shu Guo, Lihong Wang, Amin Beheshti, Philip S. Yu
Numerous methods, datasets, and evaluation metrics have been proposed in the literature, raising the need for a comprehensive and updated survey.
1 code implementation • Findings (ACL) 2021 • Jiawei Sheng, Shu Guo, Bowen Yu, Qian Li, Yiming Hei, Lihong Wang, Tingwen Liu, Hongbo Xu
Event extraction (EE) is a crucial information extraction task that aims to extract event information in texts.
1 code implementation • EMNLP 2020 • Jiawei Sheng, Shu Guo, Zhenyu Chen, Juwei Yue, Lihong Wang, Tingwen Liu, Hongbo Xu
Recent attempts solve this problem by learning static representations of entities and references, ignoring their dynamic properties, i. e., entities may exhibit diverse roles within task relations, and references may make different contributions to queries.