4.6k citations · 5.5k across the 15 of their papers we have counts for
8 papers · 1 filter
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…
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…
Explaining Deep Learning Models with Constrained Adversarial Examples
Jonathan Moore, Nils Hammerla, Chris Watkins
Machine learning algorithms generally suffer from a problem of explainability. Given a classification result from a model, it is typically hard to determine what caused the decisio…
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…
Multilingual Factor Analysis
Francisco Vargas, Kamen Brestnichki, Alex Papadopoulos-Korfiatis +1
In this work we approach the task of learning multilingual word representations in an offline manner by fitting a generative latent variable model to a multilingual dictionary. We…
Model Comparison for Semantic Grouping
Francisco Vargas, Kamen Brestnichki, Nils Hammerla
We introduce a probabilistic framework for quantifying the semantic similarity between two groups of embeddings. We formulate the task of semantic similarity as a model comparison…
Don't Settle for Average, Go for the Max: Fuzzy Sets and Max-Pooled Word Vectors
Vitalii Zhelezniak, Aleksandar Savkov, April Shen +3
Recent literature suggests that averaged word vectors followed by simple post-processing outperform many deep learning methods on semantic textual similarity tasks. Furthermore, wh…
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…