activity
20162021
most citedRepresentations of language in a model of visually grounded speech signal

133 citations · 156 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CL2021

Discrete representations in neural models of spoken language

Bertrand Higy, Lieke Gelderloos, Afra Alishahi +1

The distributed and continuous representations used by neural networks are at odds with representations employed in linguistics, which are typically symbolic. Vector quantization h…

cs.CL2020

Learning to Understand Child-directed and Adult-directed Speech

Lieke Gelderloos, Grzegorz Chrupała, Afra Alishahi

Speech directed to children differs from adult-directed speech in linguistic aspects such as repetition, word choice, and sentence length, as well as in aspects of the speech signa…

cs.CL2019

The PhotoBook Dataset: Building Common Ground through Visually-Grounded Dialogue

Janosch Haber, Tim Baumgärtner, Ece Takmaz +3

This paper introduces the PhotoBook dataset, a large-scale collection of visually-grounded, task-oriented dialogues in English designed to investigate shared dialogue history accum…

cs.CL2018

On the difficulty of a distributional semantics of spoken language

Grzegorz Chrupała, Lieke Gelderloos, Ákos Kádár +1

In the domain of unsupervised learning most work on speech has focused on discovering low-level constructs such as phoneme inventories or word-like units. In contrast, for written…

cs.CL2017★ 133 cited

Representations of language in a model of visually grounded speech signal

Grzegorz Chrupała, Lieke Gelderloos, Afra Alishahi

We present a visually grounded model of speech perception which projects spoken utterances and images to a joint semantic space. We use a multi-layer recurrent highway network to m…

cs.CL2016★ 23 cited

From phonemes to images: levels of representation in a recurrent neural model of visually-grounded language learning

Lieke Gelderloos, Grzegorz Chrupała

We present a model of visually-grounded language learning based on stacked gated recurrent neural networks which learns to predict visual features given an image description in the…