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20192021
most citedRelational Graph Attention Networks

128 citations

Showing 2019Show all

5 papers · 1 filter

cs.CL2019

Correlations between Word Vector Sets

Vitalii Zhelezniak, April Shen, Daniel Busbridge +2

Similarity measures based purely on word embeddings are comfortably competing with much more sophisticated deep learning and expert-engineered systems on unsupervised semantic text…

cs.CL20194 cited

Neural Language Priors

Joseph Enguehard, Dan Busbridge, Vitalii Zhelezniak +1

The choice of sentence encoder architecture reflects assumptions about how a sentence's meaning is composed from its constituent words. We examine the contribution of these archite…

cs.AI201912 cited

MultiVerse: Causal Reasoning using Importance Sampling in Probabilistic Programming

Yura Perov, Logan Graham, Kostis Gourgoulias +4

We elaborate on using importance sampling for causal reasoning, in particular for counterfactual inference. We show how this can be implemented natively in probabilistic programmin…

cs.CL20197 cited

Correlation Coefficients and Semantic Textual Similarity

Vitalii Zhelezniak, Aleksandar Savkov, April Shen +1

A large body of research into semantic textual similarity has focused on constructing state-of-the-art embeddings using sophisticated modelling, careful choice of learning signals…

cs.LG2019128 cited

Relational Graph Attention Networks

Dan Busbridge, Dane Sherburn, Pietro Cavallo +1

We investigate Relational Graph Attention Networks, a class of models that extends non-relational graph attention mechanisms to incorporate relational information, opening up these…