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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 Hits 1 – 20 of 31 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 SemEval-2020 Task 2: Predicting Multilingual and Cross-Lingual (Graded) Lexical Entailment ... Glavas, Goran; Vulic, Ivan; Korhonen, Anna-Leena. - : Apollo - University of Cambridge Repository, 2020 BASE Show details 3 Multi-SimLex: A Large-Scale Evaluation of Multilingual and Cross-Lingual Lexical Semantic Similarity ... Vulic, Ivan; Baker, Simon; Ponti, Edoardo. - : Apollo - University of Cambridge Repository, 2020 BASE Show details 4 Probing Pretrained Language Models for Lexical Semantics ... Vulic, Ivan; Ponti, Edoardo; Litschko, Robert. - : Apollo - University of Cambridge Repository, 2020 BASE Show details 5 The Secret is in the Spectra: Predicting Cross-Lingual Task Performance with Spectral Similarity Measures ... Dubossarsky, Haim; Vulic, Ivan; Reichart, Roi. - : Apollo - University of Cambridge Repository, 2020 BASE Show details 6 Spatial multi-arrangement for clustering and multi-way similarity dataset construction ... Majewska, Olga; McCarthy, D; Van Den Bosch, J. - : Apollo - University of Cambridge Repository, 2020 BASE Show details 7 The Secret is in the Spectra: Predicting Cross-Lingual Task Performance with Spectral Similarity Measures Dubossarsky, Haim; Vulic, Ivan; Reichart, Roi. - : Association for Computational Linguistics, 2020. : Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2020), 2020 BASE Show details 8 Spatial multi-arrangement for clustering and multi-way similarity dataset construction Majewska, Olga; McCarthy, D; van den Bosch, J. - : European Language Resources Association, 2020. : LREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings, 2020 BASE Show details 9 Probing Pretrained Language Models for Lexical Semantics Vulic, Ivan; Ponti, Edoardo; Litschko, Robert. - : Association for Computational Linguistics, 2020. : Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2020), 2020 BASE Show details 10 SemEval-2020 Task 2: Predicting Multilingual and Cross-Lingual (Graded) Lexical Entailment Glavas, Goran; Vulic, Ivan; Korhonen, Anna-Leena. - : International Committee for Computational Linguistics, 2020. : https://www.aclweb.org/anthology/2020.semeval-1.2, 2020. : Proceedings of the 14th International Workshop on Semantic Evaluation (SemEval 2020), 2020 BASE Show details 11 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 12 Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing ... Ponti, Edoardo; O'Horan, Helen; Berzak, Yevgeni. - : Apollo - University of Cambridge Repository, 2019 BASE Show details 13 Show Some Love to Your n-grams: A Bit of Progress and Stronger n-gram Language Modeling Baselines ... Shareghi, Ehsan; Gerz, Daniela; Vulic, Ivan. - : Apollo - University of Cambridge Repository, 2019 BASE Show details 14 Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing Reichart, Roi; Shutova, Ekaterina; Korhonen, Anna-Leena. - : MIT Press - Journals, 2019. : COMPUTATIONAL LINGUISTICS, 2019 BASE Show details 15 Bio-SimVerb ... Chiu, Hon Wing; Pyysalo, Sampo; Vulic, Ivan. - : Apollo - University of Cambridge Repository, 2018 BASE Show details 16 Isomorphic Transfer of Syntactic Structures in Cross-Lingual NLP ... Ponti, Edoardo; Reichart, Roi; Korhonen, Anna-Leena. - : Apollo - University of Cambridge Repository, 2018 BASE Show details 17 Language Modeling for Morphologically Rich Languages: Character-Aware Modeling for Word-Level Prediction ... Gerz, Daniela; Vulić, Ivan; Ponti, Edoardo. - : Apollo - University of Cambridge Repository, 2018 BASE Show details 18 Injecting Lexical Contrast into Word Vectors by Guiding Vector Space Specialisation ... Vulic, Ivan; Korhonen, Anna-Leena; Linguist, Assoc Computat. - : Apollo - University of Cambridge Repository, 2018 BASE Show details 19 Investigating the cross-lingual translatability of VerbNet-style classification. ... Majewska, Olga; Vulić, Ivan; McCarthy, Diana. - : Apollo - University of Cambridge Repository, 2018 BASE Show details 20 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 Page: 1 2

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