Search Results for author: William N. Havard

Found 5 papers, 2 papers with code

Contribution d’informations syntaxiques aux capacités de généralisation compositionelle des modèles seq2seq convolutifs (Assessing the Contribution of Syntactic Information for Compositional Generalization of seq2seq Convolutional Networks)

no code implementations JEP/TALN/RECITAL 2021 Diana Nicoleta Popa, William N. Havard, Maximin Coavoux, Eric Gaussier, Laurent Besacier

Le jeu de données SCAN, constitué d’un ensemble de commandes en langage naturel associées à des séquences d’action, a été spécifiquement conçu pour évaluer les capacités des réseaux de neurones à apprendre ce type de généralisation compositionnelle.

Catplayinginthesnow: Impact of Prior Segmentation on a Model of Visually Grounded Speech

no code implementations CONLL 2020 William N. Havard, Jean-Pierre Chevrot, Laurent Besacier

The language acquisition literature shows that children do not build their lexicon by segmenting the spoken input into phonemes and then building up words from them, but rather adopt a top-down approach and start by segmenting word-like units and then break them down into smaller units.

Image Retrieval Language Acquisition +1

Word Recognition, Competition, and Activation in a Model of Visually Grounded Speech

no code implementations CONLL 2019 William N. Havard, Jean-Pierre Chevrot, Laurent Besacier

In this paper, we study how word-like units are represented and activated in a recurrent neural model of visually grounded speech.

MaSS: A Large and Clean Multilingual Corpus of Sentence-aligned Spoken Utterances Extracted from the Bible

1 code implementation LREC 2020 Marcely Zanon Boito, William N. Havard, Mahault Garnerin, Éric Le Ferrand, Laurent Besacier

However, the fact that the source content (the Bible) is the same for all the languages is not exploited to date. Therefore, this article proposes to add multilingual links between speech segments in different languages, and shares a large and clean dataset of 8, 130 parallel spoken utterances across 8 languages (56 language pairs).

Automatic Speech Recognition Automatic Speech Recognition (ASR) +4

Models of Visually Grounded Speech Signal Pay Attention To Nouns: a Bilingual Experiment on English and Japanese

1 code implementation8 Feb 2019 William N. Havard, Jean-Pierre Chevrot, Laurent Besacier

We investigate the behaviour of attention in neural models of visually grounded speech trained on two languages: English and Japanese.

Retrieval

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