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Glavaš, G (6)
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Hits 1 – 6 of 6
1
Cross-lingual semantic specialization via lexical relation induction ...
Ponti, Edoardo
;
Vulić, I
;
Glavaš, G
. - : Apollo - University of Cambridge Repository, 2020
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2
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
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3
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
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4
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
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5
Specializing distributional vectors of allwords for lexical entailment ...
Kamath, A
;
Pfeiffer, J
;
Ponti, Edoardo
. - : Apollo - University of Cambridge Repository, 2019
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6
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
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