8 citations · 30 across the 6 of their papers we have counts for
9 papers
Mixture-of-Partitions: Infusing Large Biomedical Knowledge Graphs into BERT
Zaiqiao Meng, Fangyu Liu, Thomas Hikaru Clark +2
Infusing factual knowledge into pre-trained models is fundamental for many knowledge-intensive tasks. In this paper, we proposed Mixture-of-Partitions (MoP), an infusion approach t…
Few-Shot Table-to-Text Generation with Prototype Memory
Yixuan Su, Zaiqiao Meng, Simon Baker +1
Neural table-to-text generation models have achieved remarkable progress on an array of tasks. However, due to the data-hungry nature of neural models, their performances strongly…
Learning to Detect Few-Shot-Few-Clue Misinformation
Qiang Zhang, Hongbin Huang, Shangsong Liang +2
The quality of digital information on the web has been disquieting due to the lack of careful manual review. Consequently, a large volume of false textual information has been diss…
Graph Neural Pre-training for Enhancing Recommendations using Side Information
Zaiqiao Meng, Siwei Liu, Craig Macdonald +1
Leveraging the side information associated with entities (i.e. users and items) to enhance the performance of recommendation systems has been widely recognized as an important mode…
Self-Alignment Pretraining for Biomedical Entity Representations
Fangyu Liu, Ehsan Shareghi, Zaiqiao Meng +2
Despite the widespread success of self-supervised learning via masked language models (MLM), accurately capturing fine-grained semantic relationships in the biomedical domain remai…
Exploring Data Splitting Strategies for the Evaluation of Recommendation Models
Zaiqiao Meng, Richard McCreadie, Craig Macdonald +1
Effective methodologies for evaluating recommender systems are critical, so that such systems can be compared in a sound manner. A commonly overlooked aspect of recommender system…