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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 Page: 1 2 3 Hits 1 – 20 of 43 1 Shapley Idioms: Analysing BERT Sentence Embeddings for General Idiom Token Identification Nedumpozhimana, Vasudevan; Klubička, Filip; Kelleher, John D. In: Front Artif Intell (2022) BASE Show details 2 Language-Driven Region Pointer Advancement for Controllable Image Captioning ... Lindh, Annika; Ross, Robert J.; Kelleher, John D.. - : arXiv, 2020 BASE Show details 3 Semantic Relatedness and Taxonomic Word Embeddings ... Kacmajor, Magdalena; Kelleher, John D.; Klubicka, Filip. - : arXiv, 2020 BASE Show details 4 English WordNet Taxonomic Random Walk Pseudo-Corpora Klubicka, Filip; Maldonado, Alfredo; Mahalunkar, Abhijit... In: Conference papers (2020) BASE Show details 5 Language-Driven Region Pointer Advancement for Controllable Image Captioning Lindh, Annika; Ross, Robert; Kelleher, John D. In: Conference papers (2020) Abstract: Controllable Image Captioning is a recent sub-field in the multi-modal task of Image Captioning wherein constraints are placed on which regions in an image should be described in the generated natural language caption. This puts a stronger focus on producing more detailed descriptions, and opens the door for more end-user control over results. A vital component of the Controllable Image Captioning architecture is the mechanism that decides the timing of attending to each region through the advancement of a region pointer. In this paper, we propose a novel method for predicting the timing of region pointer advancement by treating the advancement step as a natural part of the language structure via a NEXT-token, motivated by a strong correlation to the sentence structure in the training data. We find that our timing agrees with the ground-truth timing in the Flickr30k Entities test data with a precision of 86.55% and a recall of 97.92%. Our model implementing this technique improves the state-of-the-art on standard captioning metrics while additionally demonstrating a considerably larger effective vocabulary size. Keyword: Artificial Intelligence and Robotics; computer vision; controllable image captioning; deep learning; machine learning; natural language generation URL: https://arrow.tudublin.ie/scschcomcon/286 https://arrow.tudublin.ie/cgi/viewcontent.cgi?article=1303&context=scschcomcon BASE Hide details 6 Local Alignment of Frame of Reference Assignment in English and Swedish Dialogue Dobnik, Simon; Kelleher, John D.; Howes, Christine In: Conference papers (2020) BASE Show details 7 Capturing and measuring thematic relatedness [<Journal>] Kacmajor, Magdalena Verfasser] DNB Subject Category Language Show details 8 Synthetic, Yet Natural: Properties of WordNet Random Walk Corpora and the impact of rare words on embedding performance Klubicka, Filip; Mahalunkar, Abhijit; Maldonado, Alfredo... In: Conference papers (2019) BASE Show details 9 Size Matters: The Impact of Training Size in Taxonomically-Enriched Word Embeddings Maldonado, Alfredo; Klubicka, Filip; Kelleher, John D. In: Articles (2019) BASE Show details 10 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 11 What is not where: the challenge of integrating spatial representations into deep learning architectures ... Kelleher, John D.; Dobnik, Simon. - : arXiv, 2018 BASE Show details 12 Is it worth it? Budget-related evaluation metrics for model selection ... Klubička, Filip; Salton, Giancarlo D.; Kelleher, John D.. - : arXiv, 2018 BASE Show details 13 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 14 Exploring the Functional and Geometric Bias of Spatial Relations Using Neural Language Models Dobnik, Simon; Ghanimifard, Mehdi; Kelleher, John D. In: Conference papers (2018) BASE Show details 15 Back to the Future: Logic and Machine Learning Kelleher, John D.; Dobnik, Simon In: Conference papers (2017) BASE Show details 16 Robot Perception Errors and Human Resolution Strategies in Situated Human-Robot Dialogue Kelleher, John D.; Mac Namee, Brian; Schütte, Niels In: Articles (2017) BASE Show details 17 Assessing the Usefulness of Different Feature Sets for Predicting the Comprehension Difficulty of Text Mac Namee, Brian; Kelleher, John D.; Fitzpatrick, Noel In: Conference papers (2017) BASE Show details 18 Towards a Computational Model of Frame of Reference Alignment in Swedish Dialogue Dobnik, Simon; Howes, Christine; Kelleher, John D.... In: Conference papers (2016) BASE Show details 19 A Model for Attention-Driven Judgements in Type Theory with Records Dobnik, Simon; Kelleher, John D. In: Conference papers (2016) BASE Show details 20 Fundamentals of Machine Learning for Neural Machine Translation Kelleher, John D. In: Conference papers (2016) BASE Show details Page: 1 2 3

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