activity
20192022
most citedMulti-task Learning via Adaptation to Similar Tasks for Mortality Prediction of Diverse Rare Diseases

10 citations · 14 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CL2022

MetaFill: Text Infilling for Meta-Path Generation on Heterogeneous Information Networks

Zequn Liu, Kefei Duan, Junwei Yang +3

Heterogeneous Information Network (HIN) is essential to study complicated networks containing multiple edge types and node types. Meta-path, a sequence of node types and edge types…

cs.CL20211 cited

Graphine: A Dataset for Graph-aware Terminology Definition Generation

Zequn Liu, Shukai Wang, Yiyang Gu +3

Precisely defining the terminology is the first step in scientific communication. Developing neural text generation models for definition generation can circumvent the labor-intens…

cs.LG202010 cited

Multi-task Learning via Adaptation to Similar Tasks for Mortality Prediction of Diverse Rare Diseases

Luchen Liu, Zequn Liu, Haoxian Wu +4

Mortality prediction of diverse rare diseases using electronic health record (EHR) data is a crucial task for intelligent healthcare. However, data insufficiency and the clinical d…

cs.LG20193 cited

Early Prediction of Sepsis From Clinical Datavia Heterogeneous Event Aggregation

Luchen Liu, Haoxian Wu, Zichang Wang +2

Sepsis is a life-threatening condition that seriously endangers millions of people over the world. Hopefully, with the widespread availability of electronic health records (EHR), p…

cs.CL2019

Learning to Customize Model Structures for Few-shot Dialogue Generation Tasks

Yiping Song, Zequn Liu, Wei Bi +2

Training the generative models with minimal corpus is one of the critical challenges for building open-domain dialogue systems. Existing methods tend to use the meta-learning frame…