Browsing by Author "Lazaridou, Angeliki"

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  • Lazaridou, Angeliki; Dinu, Georgiana; Baroni, Marco (ACL (Association for Computational Linguistics), 2015)
    Zero-shot methods in language, vision and other domains rely on a cross-space mapping function that projects vectors from the relevant feature space (e.g., visualfeature-based image representations) to a large semantic ...
  • Pham, Nghia The; Kruszewski, German; Lazaridou, Angeliki; Baroni, Marco (ACL (Association for Computational Linguistics), 2015)
    We introduce C-PHRASE, a distributional semantic model that learns word representations by optimizing context prediction for phrases at all levels in a syntactic tree, from single words to full sentences. C-PHRASE outperforms ...
  • Boleda, Gemma; Sorodoc, Ionut-Teodor; Lazaridou, Angeliki; Herbelot, Aurélie; Pezzelle, Sandro; Bernardi, Raffaella (ACL (Association for Computational Linguistics), 2016)
    In this paper, we investigate whether a neural network model can learn the meaning of natural language quantifiers (no,some and all) from their use in visual contexts. We show that memory networks perform well in this ...
  • Lazaridou, Angeliki; Chrupała, Grzegorz; Fernández, Raquel; Baroni, Marco (ACL (Association for Computational Linguistics), 2016)
    Children learn the meaning of words by being exposed to perceptually rich situations (linguistic discourse, visual scenes, etc). Current computational learning models typically simulate these rich situations through ...
  • Lazaridou, Angeliki; Marelli, Marco; Baroni, Marco (Wiley, 2017)
    By the time they reach early adulthood, English speakers are familiar with the meaning of thousands of words. In the last decades, computational simulations known as distributional semantic models (DSMs) have demonstrated ...
  • Boleda, Gemma; Paperno, Denis; Kruszewski, German; Lazaridou, Angeliki; Pham, Quan Ngoc; Bernardi, Raffaella; Pezzelle, Sandro; Baroni, Marco; Fernandez, Raquel (ACL (Association for Computational Linguistics), 2016)
    We introduce LAMBADA, a dataset to evaluate the capabilities of computational models for text understanding by means of a word prediction task. LAMBADA is a collection of narrative passages sharing the characteristic that ...
  • Lazaridou, Angeliki; Pham, Nghia The; Baroni, Marco (ACL (Association for Computational Linguistics), 2016)
    As a first step towards agents learning to communicate about their visual environment, we propose a system that, given visual representations of a referent (CAT) and a context (SOFA), identifies their discriminative ...