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Evaluating Multilingual Text Encoders for Unsupervised Cross-Lingual Retrieval ...
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Fast, Effective, and Self-Supervised: Transforming Masked Language Models into Universal Lexical and Sentence Encoders ...
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Cross-lingual semantic specialization via lexical relation induction ...
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Adversarial propagation and zero-shot cross-lingual transfer of word vector specialization ...
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Do we really need fully unsupervised cross-lingual embeddings? ...
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On the relation between linguistic typology and (limitations of) multilingual language modeling ...
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Cross-lingual semantic specialization via lexical relation induction
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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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On the relation between linguistic typology and (limitations of) multilingual language modeling
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Adversarial propagation and zero-shot cross-lingual transfer of word vector specialization
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Do we really need fully unsupervised cross-lingual embeddings?
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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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Towards zero-shot language modeling
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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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Zero-shot language transfer for cross-lingual sentence retrieval using bidirectional attention model ...
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Learning unsupervised multilingual word embeddings with incremental multilingual hubs ...
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Specializing distributional vectors of allwords for lexical entailment ...
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Investigating cross-lingual alignment methods for contextualized embeddings with Token-level evaluation ...
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Specializing distributional vectors of allwords for lexical entailment
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Investigating cross-lingual alignment methods for contextualized embeddings with Token-level evaluation
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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
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URL: https://doi.org/10.17863/CAM.44042 https://www.repository.cam.ac.uk/handle/1810/297000
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Learning unsupervised multilingual word embeddings with incremental multilingual hubs
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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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