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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 Modular Mechanistic Networks: On Bridging Mechanistic and Phenomenological Models with Deep Neural Networks in Natural Language Processing ... Dobnik, Simon; Kelleher, John D.. - : arXiv, 2018 BASE Show details 2 What is not where: the challenge of integrating spatial representations into deep learning architectures ... Kelleher, John D.; Dobnik, Simon. - : arXiv, 2018 BASE Show details 3 Is it worth it? Budget-related evaluation metrics for model selection ... Klubička, Filip; Salton, Giancarlo D.; Kelleher, John D.. - : arXiv, 2018 BASE Show details 4 Is it worth it? Budget-related evaluation metrics for model selection Klubicka, Filip; Salton, Giancarlo; Kelleher, John D. In: Conference papers (2018) BASE Show details 5 Exploring the Functional and Geometric Bias of Spatial Relations Using Neural Language Models Dobnik, Simon; Ghanimifard, Mehdi; Kelleher, John D. In: Conference papers (2018) Abstract: The challenge for computational models of spatial descriptions for situated dialogue systems is the integration of information from different modalities. The semantics of spatial descriptions are grounded in at least two sources of information: (i) a geometric representation of space and (ii) the functional interaction of related objects that. We train several neural language models on descriptions of scenes from a dataset of image captions and examine whether the functional or geometric bias of spatial descriptions reported in the literature is reflected in the estimated perplexity of these models. The results of these experiments have implications for the creation of models of spatial lexical semantics for human-robot dialogue systems. Furthermore, they also provide an insight into the kinds of the semantic knowledge captured by neural language models trained on spatial descriptions, which has implications for image captioning systems. Keyword: Artificial Intelligence and Robotics; Computational Linguistics; Computer Sciences; Linguistics URL: https://arrow.tudublin.ie/airccon/10 https://arrow.tudublin.ie/cgi/viewcontent.cgi?article=1011&context=airccon BASE Hide details

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