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Browsing Congressos (Departament de Traducció i Ciències del Llenguatge) by Subject "Deep learning"

Browsing Congressos (Departament de Traducció i Ciències del Llenguatge) by Subject "Deep learning"

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  • Sorodoc, Ionut-Teodor; Pezzelle, Sandro; Bernardi, Raffaella (ACL (Association for Computational Linguistics), 2018)
    The present work investigates whether different quantification mechanisms (set comparison, vague quantification, and proportional estimation) can be jointly learned from visual scenes by a multi-task computational model. ...
  • Boleda, Gemma; Pham, Nghia The; Kruszewski, German (ACL (Association for Computational Linguistics), 2016)
    Convolutional Neural Networks (CNNs) have shown to yield very strong results in several Computer Vision tasks. Their application to language has received much less attention, and it has mainly focused on static classification ...
  • Boleda, Gemma; Del Tredici, Marco; Fernández, Raquel (ACL (Association for Computational Linguistics), 2019)
    We present the first exploration of meaning shift over short periods of time in online communities using distributional representations. We create a small annotated dataset and use it to assess the performance of a standard ...
  • 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 ...
  • Boleda, Gemma; Aina, Laura; Silberer, Carina; Sorodoc, Ionut-Teodor; Westera, Matthijs (ACL (Association for Computational Linguistics), 2019)
    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 have been proposed. Such entity-centric ...

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