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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 – 6 of 6 1 Style versus Content: A distinction without a (learnable) difference? Jafaritazehjani, Somayeh; Lecorvé, Gwénolé; Lolive, Damien... In: International Conference on Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-03112354 ; International Conference on Computational Linguistics, Dec 2020, Virtual, Spain (2020) 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

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