110 citations · 205 across the 12 of their papers we have counts for
12 papers
Contrastive Cross-Domain Sequential Recommendation
Jiangxia Cao, Xin Cong, Jiawei Sheng +2
Cross-Domain Sequential Recommendation (CDSR) aims to predict future interactions based on user's historical sequential interactions from multiple domains. Generally, a key challen…
Towards Generalized Open Information Extraction
Bowen Yu, Zhenyu Zhang, Jingyang Li +5
Open Information Extraction (OpenIE) facilitates the open-domain discovery of textual facts. However, the prevailing solutions evaluate OpenIE models on in-domain test sets aside f…
Geometry Contrastive Learning on Heterogeneous Graphs
Shichao Zhu, Chuan Zhou, Anfeng Cheng +4
Self-supervised learning (especially contrastive learning) methods on heterogeneous graphs can effectively get rid of the dependence on supervisory data. Meanwhile, most existing r…
Cross-Domain Recommendation to Cold-Start Users via Variational Information Bottleneck
Jiangxia Cao, Jiawei Sheng, Xin Cong +2
Recommender systems have been widely deployed in many real-world applications, but usually suffer from the long-standing user cold-start problem. As a promising way, Cross-Domain R…
Deep Structural Point Process for Learning Temporal Interaction Networks
Jiangxia Cao, Xixun Lin, Xin Cong +4
This work investigates the problem of learning temporal interaction networks. A temporal interaction network consists of a series of chronological interactions between users and it…
Task-adaptive Neural Process for User Cold-Start Recommendation
Xixun Lin, Jia Wu, Chuan Zhou +3
User cold-start recommendation is a long-standing challenge for recommender systems due to the fact that only a few interactions of cold-start users can be exploited. Recent studie…
Bipartite Graph Embedding via Mutual Information Maximization
Jiangxia Cao, Xixun Lin, Shu Guo +3
Bipartite graph embedding has recently attracted much attention due to the fact that bipartite graphs are widely used in various application domains. Most previous methods, which a…
Few-Shot Event Detection with Prototypical Amortized Conditional Random Field
Xin Cong, Shiyao Cui, Bowen Yu +3
Event detection tends to struggle when it needs to recognize novel event types with a few samples. The previous work attempts to solve this problem in the identify-then-classify ma…
Graph Geometry Interaction Learning
Shichao Zhu, Shirui Pan, Chuan Zhou +3
While numerous approaches have been developed to embed graphs into either Euclidean or hyperbolic spaces, they do not fully utilize the information available in graphs, or lack the…
Coarse-to-Fine Pre-training for Named Entity Recognition
Mengge Xue, Bowen Yu, Zhenyu Zhang +3
More recently, Named Entity Recognition hasachieved great advances aided by pre-trainingapproaches such as BERT. However, currentpre-training techniques focus on building lan-guage…
Pre-training with Meta Learning for Chinese Word Segmentation
Zhen Ke, Liang Shi, Songtao Sun +3
Recent researches show that pre-trained models (PTMs) are beneficial to Chinese Word Segmentation (CWS). However, PTMs used in previous works usually adopt language modeling as pre…
Joint Extraction of Entities and Relations Based on a Novel Decomposition Strategy
Bowen Yu, Zhenyu Zhang, Xiaobo Shu +4
Joint extraction of entities and relations aims to detect entity pairs along with their relations using a single model. Prior work typically solves this task in the extract-then-cl…