23 citations · 27 across the 6 of their papers we have counts for
7 papers
GIMLET: A Unified Graph-Text Model for Instruction-Based Molecule Zero-Shot Learning
Haiteng Zhao, Shengchao Liu, Chang Ma +5
Molecule property prediction has gained significant attention in recent years. The main bottleneck is the label insufficiency caused by expensive lab experiments. In order to allev…
Interactive Natural Language Processing
Zekun Wang, Ge Zhang, Kexin Yang +19
Interactive Natural Language Processing (iNLP) has emerged as a novel paradigm within the field of NLP, aimed at addressing limitations in existing frameworks while aligning with t…
An Empirical Study of Retrieval-enhanced Graph Neural Networks
Dingmin Wang, Shengchao Liu, Hanchen Wang +5
Graph Neural Networks (GNNs) are effective tools for graph representation learning. Most GNNs rely on a recursive neighborhood aggregation scheme, named message passing, thereby th…
Relational Memory Augmented Language Models
Qi Liu, Dani Yogatama, Phil Blunsom
We present a memory-augmented approach to condition an autoregressive language model on a knowledge graph. We represent the graph as a collection of relation triples and retrieve r…
Tiny-NewsRec: Effective and Efficient PLM-based News Recommendation
Yang Yu, Fangzhao Wu, Chuhan Wu +2
News recommendation is a widely adopted technique to provide personalized news feeds for the user. Recently, pre-trained language models (PLMs) have demonstrated the great capabili…
NewsBERT: Distilling Pre-trained Language Model for Intelligent News Application
Chuhan Wu, Fangzhao Wu, Yang Yu +3
Pre-trained language models (PLMs) like BERT have made great progress in NLP. News articles usually contain rich textual information, and PLMs have the potentials to enhance news t…
A Survey on Contextual Embeddings
Qi Liu, Matt J. Kusner, Phil Blunsom
Contextual embeddings, such as ELMo and BERT, move beyond global word representations like Word2Vec and achieve ground-breaking performance on a wide range of natural language proc…