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1
Evaluating Multilingual Text Encoders for Unsupervised Cross-Lingual Retrieval ...
Litschko, R; Vulić, I; Ponzetto, SP. - : Apollo - University of Cambridge Repository, 2021
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2
Fast, Effective, and Self-Supervised: Transforming Masked Language Models into Universal Lexical and Sentence Encoders ...
Liu, Fangyu; Vulić, I; Korhonen, Anna-Leena. - : Apollo - University of Cambridge Repository, 2021
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3
Towards zero-shot language modeling ...
Ponti, Edoardo; Vulić, I; Cotterell, R. - : Apollo - University of Cambridge Repository, 2020
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4
Cross-lingual semantic specialization via lexical relation induction ...
Ponti, Edoardo; Vulić, I; Glavaš, G. - : Apollo - University of Cambridge Repository, 2020
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5
Adversarial propagation and zero-shot cross-lingual transfer of word vector specialization ...
Ponti, Edoardo; Vulić, I; Glavaš, G. - : Apollo - University of Cambridge Repository, 2020
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6
Do we really need fully unsupervised cross-lingual embeddings? ...
Vulić, I; Glavaš, G; Reichart, R. - : Apollo - University of Cambridge Repository, 2020
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7
On the relation between linguistic typology and (limitations of) multilingual language modeling ...
Gerz, Daniela; Vulić, I; Ponti, Edoardo. - : Apollo - University of Cambridge Repository, 2020
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8
Cross-lingual semantic specialization via lexical relation induction
Ponti, Edoardo; Vulić, I; Glavaš, G. - : 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
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9
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
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10
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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11
Do we really need fully unsupervised cross-lingual embeddings?
Vulić, I; Glavaš, G; Reichart, 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
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12
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
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13
A survey of cross-lingual word embedding models ...
Ruder, S; Vulić, I; Søgaard, A. - : Apollo - University of Cambridge Repository, 2019
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14
Zero-shot language transfer for cross-lingual sentence retrieval using bidirectional attention model ...
Glavaš, G; Vulić, I. - : Apollo - University of Cambridge Repository, 2019
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15
Learning unsupervised multilingual word embeddings with incremental multilingual hubs ...
Heyman, G; Verreet, B; Vulić, I. - : Apollo - University of Cambridge Repository, 2019
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16
Specializing distributional vectors of allwords for lexical entailment ...
Kamath, A; Pfeiffer, J; Ponti, Edoardo. - : Apollo - University of Cambridge Repository, 2019
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17
Investigating cross-lingual alignment methods for contextualized embeddings with Token-level evaluation ...
Liu, Qianchu; McCarthy, D; Vulić, I. - : Apollo - University of Cambridge Repository, 2019
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18
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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19
Investigating cross-lingual alignment methods for contextualized embeddings with Token-level evaluation
Liu, Qianchu; McCarthy, D; Vulić, I; Korhonen, Anna-Leena. - : CoNLL 2019 - 23rd Conference on Computational Natural Language Learning, Proceedings of the Conference, 2019
Abstract: In this paper, we present a thorough investigation on methods that align pre-trained contextualized embeddings into shared cross-lingual context-aware embedding space, providing strong reference benchmarks for future context-aware crosslingual models. We propose a novel and challenging task, Bilingual Token-level Sense Retrieval (BTSR). It specifically evaluates the accurate alignment of words with the same meaning in cross-lingual non-parallel contexts, currently not evaluated by existing tasks such as Bilingual Contextual Word Similarity and Sentence Retrieval. We show how the proposed BTSR task highlights the merits of different alignment methods. In particular, we find that using context average type-level alignment is effective in transferring monolingual contextualized embeddings cross-lingually especially in non-parallel contexts, and at the same time improves the monolingual space. Furthermore, aligning independently trained models yields better performance than aligning multilingual embeddings with shared vocabulary. ; Peterhouse College Studentship; ERC Consolidator Grant LEXICAL
URL: https://doi.org/10.17863/CAM.44042
https://www.repository.cam.ac.uk/handle/1810/297000
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20
Learning unsupervised multilingual word embeddings with incremental multilingual hubs
Heyman, G; Verreet, B; Vulić, I. - : NAACL HLT 2019 - 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference, 2019
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