Ivan Titov

TL;DR: It is shown that factorization models for link prediction such as DistMult can be significantly improved through the use of an R-GCN encoder model to accumulate evidence over multiple inference steps in the graph, demonstrating a large improvement of 29.8% on FB15k-237 over a decoder-only baseline.

TL;DR: Relational Graph Convolutional Networks (R-GCNets) as discussed by the authors are related to a recent class of neural networks operating on graphs, and are developed specifically to deal with the highly multi-relational data characteristic of realistic knowledge bases.

TL;DR: It is found that the most important and confident heads play consistent and often linguistically-interpretable roles and when pruning heads using a method based on stochastic gates and a differentiable relaxation of the L0 penalty, it is observed that specialized heads are last to be pruned.

TL;DR: This paper presents a novel framework for extracting ratable aspects of objects from online user reviews and argues that multi-grain models are more appropriate for this task since standard models tend to produce topics that correspond to global properties of objects rather than aspects of an object that tend to be rated by a user.

TL;DR: A statistical model is proposed which is able to discover corresponding topics in text and extract textual evidence from reviews supporting each of these aspect ratings, a fundamental problem in aspect-based sentiment summarization.