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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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2
Towards zero-shot language modeling ...
Ponti, Edoardo; Vulić, I; Cotterell, R. - : Apollo - University of Cambridge Repository, 2020
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3
Cross-lingual semantic specialization via lexical relation induction ...
Ponti, Edoardo; Vulić, I; Glavaš, G. - : Apollo - University of Cambridge Repository, 2020
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4
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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5
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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6
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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7
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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8
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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9
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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10
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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11
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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12
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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13
Investigating cross-lingual alignment methods for contextualized embeddings with Token-level evaluation
Liu, Qianchu; McCarthy, D; Vulić, I. - : CoNLL 2019 - 23rd Conference on Computational Natural Language Learning, Proceedings of the Conference, 2019
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14
A systematic study of leveraging subword information for learning word representations
Zhu, Y; Korhonen, Anna-Leena; 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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15
Acquiring verb classes through bottom-up semantic verb clustering ...
Majewska, Olga; McCarthy, D; Vulić, I. - : Apollo - University of Cambridge Repository, 2018
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16
Acquiring verb classes through bottom-up semantic verb clustering
Majewska, Olga; McCarthy, D; Vulić, I. - : LREC 2018 - 11th International Conference on Language Resources and Evaluation, 2018
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17
Decoding sentiment from distributed representations of sentences ...
Ponti, Edoardo; Vulić, I; Korhonen, Anna-Leena. - : Apollo - University of Cambridge Repository, 2017
Abstract: Distributed representations of sentences have been developed recently to represent their meaning as real-valued vectors. However, it is not clear how much information such representations retain about the polarity of sentences. To study this question, we decode sentiment from unsupervised sentence representations learned with different architectures (sensitive to the order of words, the order of sentences, or none) in 9 typologically diverse languages. Sentiment results from the (recursive) composition of lexical items and grammatical strategies such as negation and concession. The results are manifold: we show that there is no `one-size-fits-all' representation architecture outperforming the others across the board. Rather, the top-ranking architectures depend on the language and data at hand. Moreover, we find that in several cases the additive composition model based on skip-gram word vectors may surpass supervised state-of-art architectures such as bidirectional LSTMs. Finally, we provide a possible ...
URL: https://www.repository.cam.ac.uk/handle/1810/269691
https://dx.doi.org/10.17863/cam.10796
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18
HyperLex: A Large-Scale Evaluation of Graded Lexical Entailment ...
Vulić, I; Gerz, D; Kiela, D. - : Apollo - University of Cambridge Repository, 2017
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19
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
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20
HyperLex: A Large-Scale Evaluation of Graded Lexical Entailment
Vulić, I; Gerz, D; Kiela, D. - : MIT Press, 2017. : Computational Linguistics, 2017
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