83 citations · 536 across the 64 of their papers we have counts for
7 papers · 1 filter
Embedding Words as Distributions with a Bayesian Skip-gram Model
Arthur Bražinskas, Serhii Havrylov, Ivan Titov
We introduce a method for embedding words as probability densities in a low-dimensional space. Rather than assuming that a word embedding is fixed across the entire text collection…
Graph Convolutional Encoders for Syntax-aware Neural Machine Translation
Jasmijn Bastings, Ivan Titov, Wilker Aziz +2
We present a simple and effective approach to incorporating syntactic structure into neural attention-based encoder-decoder models for machine translation. We rely on graph-convolu…
Optimizing Differentiable Relaxations of Coreference Evaluation Metrics
Phong Le, Ivan Titov
Coreference evaluation metrics are hard to optimize directly as they are non-differentiable functions, not easily decomposable into elementary decisions. Consequently, most approac…
Modeling Relational Data with Graph Convolutional Networks
Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem +3
Knowledge graphs enable a wide variety of applications, including question answering and information retrieval. Despite the great effort invested in their creation and maintenance,…
Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling
Diego Marcheggiani, Ivan Titov
Semantic role labeling (SRL) is the task of identifying the predicate-argument structure of a sentence. It is typically regarded as an important step in the standard NLP pipeline.…
Modeling Semantic Expectation: Using Script Knowledge for Referent Prediction
Ashutosh Modi, Ivan Titov, Vera Demberg +2
Recent research in psycholinguistics has provided increasing evidence that humans predict upcoming content. Prediction also affects perception and might be a key to robustness in h…