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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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Abstract:
Semantic \specialization is a process of fine-tuning pre-trained distributional word vectors using external lexical knowledge (e.g., WordNet) to accentuate a particular semantic relation in the specialized vector space. While post-processing specialization methods are applicable to arbitrary distributional vectors, they are limited to updating only the vectors of words occurring in external lexicons (i.e., seen words), leaving the vectors of all other words unchanged. We propose a novel approach to specializing the full distributional vocabulary. Our adversarial post-specialization method propagates the external lexical knowledge to the full distributional space. We exploit words seen in the resources as training examples for learning a global specialization function. This function is learned by combining a standard L2-distance loss with a adversarial loss: the adversarial component produces more realistic output vectors. We show the effectiveness and robustness of the proposed method across three languages ...
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URL: https://www.repository.cam.ac.uk/handle/1810/287860 https://dx.doi.org/10.17863/cam.35175
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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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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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