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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 – 16 of 16 1 Delving Deeper into Cross-lingual Visual Question Answering ... Liu, Chen; Pfeiffer, Jonas; Korhonen, Anna. - : arXiv, 2022 BASE Show details 2 Semantic Data Set Construction from Human Clustering and Spatial Arrangement ... Majewska, Olga; McCarthy, Diana; Van Den Bosch, Jasper JF. - : Apollo - University of Cambridge Repository, 2021 BASE Show details 3 Multi-SimLex: A Large-Scale Evaluation of Multilingual and Cross-Lingual Lexical Semantic Similarity Vulic, Ivan; Baker, Simon; Ponti, Edoardo Maria... In: ISSN: 0891-2017 ; EISSN: 1530-9312 ; Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-02975786 ; Computational Linguistics, Massachusetts Institute of Technology Press (MIT Press), 2020, 46 (4), pp.847-897 ; https://direct.mit.edu/coli/article/46/4/847/97326/Multi-SimLex-A-Large-Scale-Evaluation-of (2020) BASE Show details 4 Multidirectional Associative Optimization of Function-Specific Word Representations ... Gerz, Daniela; Vulic, Ivan; Rei, Marek. - : Apollo - University of Cambridge Repository, 2020 BASE Show details 5 Manual Clustering and Spatial Arrangement of Verbs for Multilingual Evaluation and Typology Analysis ... Majewska, Olga; Vulic, Ivan; McCarthy, Diana. - : Apollo - University of Cambridge Repository, 2020 BASE Show details 6 Emergent Communication Pretraining for Few-Shot Machine Translation ... Li, Yaoyiran; Ponti, Edoardo; Vulic, Ivan. - : Apollo - University of Cambridge Repository, 2020 BASE Show details 7 Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity ... Lauscher, Anne; Vulic, Ivan; Ponti, Edoardo. - : Apollo - University of Cambridge Repository, 2020 BASE Show details 8 Classification-Based Self-Learning for Weakly Supervised Bilingual Lexicon Induction ... Karan, Mladen; Vulic, Ivan; Korhonen, Anna. - : Apollo - University of Cambridge Repository, 2020 BASE Show details 9 Improving Bilingual Lexicon Induction with Unsupervised Post-Processing of Monolingual Word Vector Spaces ... Vulic, Ivan; Korhonen, Anna; Glavas, Goran. - : Apollo - University of Cambridge Repository, 2020 BASE Show details 10 Improving Bilingual Lexicon Induction with Unsupervised Post-Processing of Monolingual Word Vector Spaces Vulic, Ivan; Korhonen, Anna; Glavas, Goran. - : 5TH WORKSHOP ON REPRESENTATION LEARNING FOR NLP (REPL4NLP-2020), 2020 BASE Show details 11 Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity Lauscher, Anne; Vulic, Ivan; Ponti, Edoardo. - : International Committee on Computational Linguistics, 2020. : https://www.aclweb.org/anthology/2020.coling-main.118, 2020. : Proceedings of the 28th International Conference on Computational Linguistics (COLING 2020), 2020 BASE Show details 12 Manual Clustering and Spatial Arrangement of Verbs for Multilingual Evaluation and Typology Analysis Majewska, Olga; Vulic, Ivan; McCarthy, Diana; Korhonen, Anna. - : International Committee on Computational Linguistics, 2020. : https://www.aclweb.org/anthology/2020.coling-main.423, 2020. : Proceedings of the 28th International Conference on Computational Linguistics (COLING 2020), 2020 Abstract: We present the first evaluation of the applicability of a spatial arrangement method (SpAM) to a typologically diverse language sample, and its potential to produce semantic evaluation resources to support multilingual NLP, with a focus on verb semantics. We demonstrate SpAM’s utility in allowing for quick bottom-up creation of large-scale evaluation datasets that balance cross-lingual alignment with language specificity. Starting from a shared sample of 825 English verbs, translated into Chinese, Japanese, Finnish, Polish, and Italian, we apply a two-phase annotation process which produces (i) semantic verb classes and (ii) fine-grained similarity scores for nearly 130 thousand verb pairs. We use the two types of verb data to (a) examine cross-lingual similarities and variation, and (b) evaluate the capacity of static and contextualised representation models to accurately reflect verb semantics, contrasting the performance of large language-specific pretraining models with their multilingual equivalent on semantic clustering and lexical similarity, across different domains of verb meaning. We release the data from both phases as a large-scale multilingual resource, comprising 85 verb classes and nearly 130k pairwise similarity scores, offering a wealth of possibilities for further evaluation and research on multilingual verb semantics. URL: https://doi.org/10.17863/CAM.62213 https://www.repository.cam.ac.uk/handle/1810/315106 BASE Hide details 13 Classification-Based Self-Learning for Weakly Supervised Bilingual Lexicon Induction Karan, Mladen; Vulic, Ivan; Korhonen, Anna. - : Association for Computational Linguistics, 2020 BASE Show details 14 Emergent Communication Pretraining for Few-Shot Machine Translation Vulic, Ivan; Ponti, Edoardo; Korhonen, Anna. - : International Committee on Computational Linguistics, 2020. : https://www.aclweb.org/anthology/2020.coling-main.416, 2020. : Proceedings of the 28th International Conference on Computational Linguistics (COLING 2020), 2020 BASE Show details 15 Multidirectional Associative Optimization of Function-Specific Word Representations Gerz, Daniela; Vulic, Ivan; Rei, Marek. - : Association for Computational Linguistics, 2020. : 58TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2020), 2020 BASE Show details 16 Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing Ponti, Edoardo,; O 'horan, Helen; Berzak, Yevgeni... In: https://hal.archives-ouvertes.fr/hal-01856176 ; 2018 (2018) BASE Show details

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