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Browsing Congressos (Departament de Traducció i Ciències del Llenguatge) by Author "Baroni, Marco"

Browsing Congressos (Departament de Traducció i Ciències del Llenguatge) by Author "Baroni, Marco"

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  • Cheng, Emily; Kervadec, Corentin; Baroni, Marco (ACL (Association for Computational Linguistics), 2023)
    For a language model (LM) to faithfully model human language, it must compress vast, potentially infinite information into relatively few dimensions. We propose analyzing compression in (pre-trained) LMs from two points ...
  • Rakotonirina, Nathanael Carraz; Dessì, Roberto; Petroni, Fabio; Riedel, Sebastian; Baroni, Marco (International Conference on Learning Representations (ICLR), 2023)
    We study whether automatically-induced prompts that effectively extract information from a language model can also be used, out-of-the-box, to probe other language models for the same information. After confirming that ...
  • Dessi, Roberto; Baroni, Marco (ACL (Association for Computational Linguistics), 2019)
    Lake and Baroni (2018) introduced the SCAN dataset probing the ability of seq2seq models to capture compositional generalizations, such as inferring the meaning of “jump around” 0- shot from the component words. ...
  • Dessì, Roberto; Gualdoni, Eleonora; Franzon, Francesca; Boleda, Gemma; Baroni, Marco (ACL (Association for Computational Linguistics), 2022)
    We compare the 0-shot performance of a neural caption-based image retriever when given as input either human-produced captions or captions generated by a neural captioner. We conduct this comparison on the recently ...
  • Sorodoc, Ionut-Teodor; Boleda, Gemma; Baroni, Marco (ACL (Association for Computational Linguistics), 2021)
    In recent years, the NLP community has shown increasing interest in analysing how deep learning models work. Given that large models trained on complex tasks are difficult to inspect, some of this work has focused ...
  • Dessì, Roberto; Bevilacqua, Michele; Gualdoni, Eleonora; Rakotonirina, Nathanael Carraz; Franzon, Francesca; Baroni, Marco (Institute of Electrical and Electronics Engineers (IEEE), 2023)
    Neural captioners are typically trained to mimic humangenerated references without optimizing for any specific communication goal, leading to problems such as the generation of vague captions. In this paper, we show ...
  • Boleda, Gemma; Gupta, Abhijeet; Baroni, Marco; Padó, Sebastian (ACL (Association for Computational Linguistics), 2015)
    Distributional methods have proven to excel at capturing fuzzy, graded aspects of meaning (Italy is more similar to Spain than to Germany). In contrast, it is difficult to extract the values of more specific attributes of ...
  • Baroni, Marco; Lenci, Alessandro (ACL (Association for Computational Linguistics), 2009)
    Mitchell et al. (2008) demonstrated that corpus-extracted models of semantic knowledge can predict neural activation patterns recorded using fMRI. This could be a very powerful technique for evaluating conceptual models ...
  • Kharitonov, Eugene; Baroni, Marco (ACL (Association for Computational Linguistics), 2020)
    Studies of discrete languages emerging when neural agents communicate to solve a joint task often look for evidence of compositional structure. This stems for the expectation that such a structure would allow languages ...
  • Herbelot, Aurélie; Baroni, Marco (ACL (Association for Computational Linguistics), 2018)
    Distributional semantics models are known to struggle with small data. It is generally accepted that in order to learn ‘a good vector’ for a word, a model must have sufficient examples of its usage. This contradicts the ...
  • 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 ...
  • Boleda, Gemma; Baroni, Marco; Pham, Nghia The; McNally, Louise, 1965- (ACL (Association for Computational Linguistics), 2013)
    Distributional semantics has very successfully modeled semantic phenomena at the word level, and recently interest has grown in extending it to capture the meaning of phrases via semantic composition. We present experiments ...
  • Dessì, Roberto; Kharitonov, Eugene; Baroni, Marco (Neural Information Processing Systems (NeurIPS), 2021)
    As deep networks begin to be deployed as autonomous agents, the issue of how they can communicate with each other becomes important. Here, we train two deep nets from scratch to perform large-scale referent identification ...
  • 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 ...
  • Baroni, Marco; Kilgarriff, Adam (ACL (Association for Computational Linguistics), 2006)
    The Web contains vast amounts of linguistic data. One key issue for linguists and language technologists is how to access it. Commercial search engines give highly compromised access. An alternative is to crawl the Web ...
  • Ritter, Samuel; Long, Cotie; Paperno, Denis; Baroni, Marco; Botvinick, Matthew; Goldberg, Adele (ACL (Association for Computational Linguistics), 2015)
    Complex interactions among the meanings of words are important factors in the function that maps word meanings to phrase meanings. Recently, compositional distributional semantics models (CDSM) have been designed with the ...
  • Boleda, Gemma; Padó, Sebastian; Pham, Nghia The; Baroni, Marco (ACL (Association for Computational Linguistics), 2017)
    Reference is the crucial property of language that allows us to connect linguistic expressions to the world. Modeling it requires handling both continuous and discrete aspects of meaning. Data-driven models excel at the ...
  • 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 ...
  • Blasi, Damian; Cotterell, Ryan; Wolf-Sonkin, Lawrence; Stoll, Sabine; Bickel, Balthasar; Baroni, Marco (ACL (Association for Computational Linguistics), 2019)
    Embedding a clause inside another (“the girl [who likes cars [that run fast]] has arrived”) is a fundamental resource that has been argued to be a key driver of linguistic expressiveness. As such, it plays a central role ...
  • Mahaut, Matéo; Franzon, Francesca; Dessì, Roberto; Baroni, Marco (International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), 2023-07-31)
    As large pre-trained image-processing neural networks are being embedded in autonomous agents such as self-driving cars or robots, the question arises of how such systems can communicate with each other about the ...

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