14 citations · 14 across the 2 of their papers we have counts for
10 papers
Deep daxes: Mutual exclusivity arises through both learning biases and pragmatic strategies in neural networks
Kristina Gulordava, Thomas Brochhagen, Gemma Boleda
Children's tendency to associate novel words with novel referents has been taken to reflect a bias toward mutual exclusivity. This tendency may be advantageous both as (1) an ad-ho…
Recurrent Instance Segmentation using Sequences of Referring Expressions
Alba Herrera-Palacio, Carles Ventura, Carina Silberer +3
The goal of this work is to segment the objects in an image that are referred to by a sequence of linguistic descriptions (referring expressions). We propose a deep neural network…
Putting words in context: LSTM language models and lexical ambiguity
Laura Aina, Kristina Gulordava, Gemma Boleda
In neural network models of language, words are commonly represented using context-invariant representations (word embeddings) which are then put in context in the hidden layers. S…
Don't Blame Distributional Semantics if it can't do Entailment
Matthijs Westera, Gemma Boleda
Distributional semantics has had enormous empirical success in Computational Linguistics and Cognitive Science in modeling various semantic phenomena, such as semantic similarity,…
What do Entity-Centric Models Learn? Insights from Entity Linking in Multi-Party Dialogue
Laura Aina, Carina Silberer, Matthijs Westera +2
Humans use language to refer to entities in the external world. Motivated by this, in recent years several models that incorporate a bias towards learning entity representations ha…
Distributional Semantics and Linguistic Theory
Gemma Boleda
Distributional semantics provides multi-dimensional, graded, empirically induced word representations that successfully capture many aspects of meaning in natural languages, as sho…