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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 – 12 of 12 1 Towards zero-shot language modeling ... Ponti, Edoardo; Vulić, I; Cotterell, R. - : Apollo - University of Cambridge Repository, 2020 BASE Show details 2 Cross-lingual semantic specialization via lexical relation induction ... Ponti, Edoardo; Vulić, I; Glavaš, G. - : Apollo - University of Cambridge Repository, 2020 BASE Show details 3 Adversarial propagation and zero-shot cross-lingual transfer of word vector specialization ... Ponti, Edoardo; Vulić, I; Glavaš, G. - : Apollo - University of Cambridge Repository, 2020 BASE Show details 4 On the relation between linguistic typology and (limitations of) multilingual language modeling ... Gerz, Daniela; Vulić, I; Ponti, Edoardo. - : Apollo - University of Cambridge Repository, 2020 BASE Show details 5 Cross-lingual semantic specialization via lexical relation induction Ponti, Edoardo; Vulić, I; Glavaš, G; Reichart, R; Korhonen, Anna-Leena. - : EMNLP-IJCNLP 2019 - 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, Proceedings of the Conference, 2020 Abstract: Semantic specialization integrates structured linguistic knowledge from external resources (such as lexical relations in WordNet) into pretrained distributional vectors in the form of constraints. However, this technique cannot be leveraged in many languages, because their structured external resources are typically incomplete or non-existent. To bridge this gap, we propose a novel method that transfers specialization from a resource-rich source language (English) to virtually any target language. Our specialization transfer comprises two crucial steps: 1) Inducing noisy constraints in the target language through automatic word translation; and 2) Filtering the noisy constraints via a state-of-the-art relation prediction model trained on the source language constraints. This allows us to specialize any set of distributional vectors in the target language with the refined constraints. We prove the effectiveness of our method through intrinsic word similarity evaluation in 8 languages, and with 3 downstream tasks in 5 languages: lexical simplification, dialog state tracking, and semantic textual similarity. The gains over the previous state-of-art specialization methods are substantial and consistent across languages. Our results also suggest that the transfer method is effective even for lexically distant source-target language pairs. Finally, as a by-product, our method produces lists of WordNet-style lexical relations in resource-poor languages. URL: https://doi.org/10.17863/CAM.43734 https://www.repository.cam.ac.uk/handle/1810/296686 BASE Hide details 6 On the relation between linguistic typology and (limitations of) multilingual language modeling Gerz, Daniela; Vulić, I; Ponti, Edoardo. - : Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018, 2020 BASE Show details 7 Adversarial propagation and zero-shot cross-lingual transfer of word vector specialization Ponti, Edoardo; Vulić, I; Glavaš, G. - : Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018, 2020 BASE Show details 8 Towards zero-shot language modeling Ponti, Edoardo; Vulić, I; Cotterell, R. - : EMNLP-IJCNLP 2019 - 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, Proceedings of the Conference, 2020 BASE Show details 9 Specializing distributional vectors of allwords for lexical entailment ... Kamath, A; Pfeiffer, J; Ponti, Edoardo. - : Apollo - University of Cambridge Repository, 2019 BASE Show details 10 Specializing distributional vectors of allwords for lexical entailment Kamath, A; Pfeiffer, J; Ponti, Edoardo. - : ACL 2019 - 4th Workshop on Representation Learning for NLP, RepL4NLP 2019 - Proceedings of the Workshop, 2019 BASE Show details 11 Decoding sentiment from distributed representations of sentences ... Ponti, Edoardo; Vulić, I; Korhonen, Anna-Leena. - : Apollo - University of Cambridge Repository, 2017 BASE Show details 12 Decoding sentiment from distributed representations of sentences Ponti, Edoardo; Vulić, I; Korhonen, Anna-Leena. - : Association for Computational Linguistics, 2017. : *SEM 2017 - 6th Joint Conference on Lexical and Computational Semantics, Proceedings, 2017 BASE Show details

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