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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 5 of 5 1 Language Modelling as a Multi-Task Problem ... Weber, Lucas; Jumelet, Jaap; Bruni, Elia. - : arXiv, 2021 BASE Show details 2 Assessing incrementality in sequence-to-sequence models ... Ulmer, Dennis; Hupkes, Dieuwke; Bruni, Elia. - : arXiv, 2019 BASE Show details 3 Compositionality decomposed: how do neural networks generalise? ... Hupkes, Dieuwke; Dankers, Verna; Mul, Mathijs; Bruni, Elia. - : arXiv, 2019 Abstract: Despite a multitude of empirical studies, little consensus exists on whether neural networks are able to generalise compositionally, a controversy that, in part, stems from a lack of agreement about what it means for a neural model to be compositional. As a response to this controversy, we present a set of tests that provide a bridge between, on the one hand, the vast amount of linguistic and philosophical theory about compositionality of language and, on the other, the successful neural models of language. We collect different interpretations of compositionality and translate them into five theoretically grounded tests for models that are formulated on a task-independent level. In particular, we provide tests to investigate (i) if models systematically recombine known parts and rules (ii) if models can extend their predictions beyond the length they have seen in the training data (iii) if models' composition operations are local or global (iv) if models' predictions are robust to synonym substitutions and ... Keyword: Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG; Machine Learning stat.ML URL: https://dx.doi.org/10.48550/arxiv.1908.08351 https://arxiv.org/abs/1908.08351 BASE Hide details 4 Beyond task success: A closer look at jointly learning to see, ask, and GuessWhat ... Shekhar, Ravi; Venkatesh, Aashish; Baumgärtner, Tim. - : arXiv, 2018 BASE Show details 5 Reading visually embodied meaning from the brain: Visually grounded computational models decode visual-object mental imagery induced by written text Anderson, Andrew James; Bruni, Elia; Lopopolo, Alessandro. - : Elsevier BV, 2015 BASE Show details

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