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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 – 3 of 3 1 XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning ... Ponti, Edoardo; Glavaš, Goran; Majewska, Olga. - : Apollo - University of Cambridge Repository, 2020 BASE Show details 2 XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning Liu, Qianchu; Korhonen, Anna-Leena; Majewska, Olga. - : Association for Computational Linguistics, 2020. : Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2020), 2020 BASE Show details 3 Post-Specialisation: Retrofitting Vectors of Words Unseen in Lexical Resources ... Vulic, Ivan; Glavaš, Goran; Mrkšić, Nikola; Korhonen, Anna-Leena. - : Apollo - University of Cambridge Repository, 2018 Abstract: Word vector specialisation (also known as retrofitting) is a portable, light-weight approach to fine-tuning arbitrary distributional word vector spaces by injecting external knowledge from rich lexical resources such as WordNet. By design, these post-processing methods only update the vectors of words occurring in external lexicons, leaving the representations of all unseen words intact. In this paper, we show that constraint-driven vector space specialisation can be extended to unseen words. We propose a novel post-specialisation method that: a) preserves the useful linguistic knowledge for seen words; while b) propagating this external signal to unseen words in order to improve their vector representations as well. Our post-specialisation approach explicits a non-linear specialisation function in the form of a deep neural network by learning to predict specialised vectors from their original distributional counterparts. The learned function is then used to specialise vectors of unseen words. This approach, ... : https://www.aclweb.org/anthology/N18-1048 ... URL: https://www.repository.cam.ac.uk/handle/1810/294076 https://dx.doi.org/10.17863/cam.41176 BASE Hide details

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