activity
20202022
most citedDeep Structural Point Process for Learning Temporal Interaction Networks

3 citations · 5 across the 4 of their papers we have counts for

collaborators

5 papers

cs.IR20222 cited

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…

cs.CL2022

Document-Level Event Extraction via Human-Like Reading Process

Shiyao Cui, Xin Cong, Bowen Yu +3

Document-level Event Extraction (DEE) is particularly tricky due to the two challenges it poses: scattering-arguments and multi-events. The first challenge means that arguments of…

cs.CL2021

Enhanced Language Representation with Label Knowledge for Span Extraction

Pan Yang, Xin Cong, Zhenyun Sun +1

Span extraction, aiming to extract text spans (such as words or phrases) from plain texts, is a fundamental process in Information Extraction. Recent works introduce the label know…

cs.SI20213 cited

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…

cs.CL2020

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…