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Hits 1 – 10 of 10
1
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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2
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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3
Automatic detection and correction of context-dependent dt-mistakes using neural networks ...
Heyman, G
;
Vulić, I
;
Laevaert, Y
. - : Apollo - University of Cambridge Repository, 2018
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4
Automatic detection and correction of context-dependent dt-mistakes using neural networks
Heyman, G
;
Vulić, I
;
Laevaert, Y
. - : Computational Linguistics in the Netherlands Journal, 2018
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5
Bilingual lexicon induction by learning to combine word-level and character-level representations ...
Heyman, G
;
Vulíc, I
;
Moens, MF
. - : Apollo - University of Cambridge Repository, 2017
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6
Bilingual lexicon induction by learning to combine word-level and character-level representations
Heyman, G
;
Vulíc, I
;
Moens, MF
. - : Association for Computational Linguistics, 2017. : 15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017 - Proceedings of Conference, 2017
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7
Bilingual distributed word representations from document-aligned comparable data ...
Vulić, I
;
Moens, MF
. - : Apollo - University of Cambridge Repository, 2016
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8
Multi-Modal Representations for Improved Bilingual Lexicon Learning ...
Vulić, I
;
Kiela, D
;
Clark, Stephen
. - : Apollo - University of Cambridge Repository, 2016
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9
Bilingual distributed word representations from document-aligned comparable data
Vulić, I
;
Moens, MF
. - : AI Access Foundation, 2016. : https://jair.org/index.php/jair/article/view/10997, 2016. : Journal of Artificial Intelligence Research, 2016
Abstract:
We propose a new model for learning bilingual word representations from non-parallel document-aligned data. Following the recent advances in word representation learning, our model learns dense real-valued word vectors, that is, bilingual word embeddings (BWEs). Unlike prior work on inducing BWEs which heavily relied on parallel sentence-aligned corpora and/or readily available translation resources such as dictionaries, the article reveals that BWEs may be learned solely on the basis of document-aligned comparable data without any additional lexical resources nor syntactic information. We present a comparison of our approach with previous state-of-the-art models for learning bilingual word representations from comparable data that rely on the framework of multilingual probabilistic topic modeling (MuPTM), as well as with distributional local context-counting models. We demonstrate the utility of the induced BWEs in two semantic tasks: (1) bilingual lexicon extraction, (2) suggesting word translations in context for polysemous words. Our simple yet effective BWE-based models significantly outperform the MuPTM-based and contextcounting representation models from comparable data as well as prior BWE-based models, and acquire the best reported results on both tasks for all three tested language pairs. ; This work was done while Ivan Vuli c was a postdoctoral researcher at Department of Computer Science, KU Leuven supported by the PDM Kort fellowship (PDMK/14/117). The work was also supported by the SCATE project (IWT-SBO 130041) and the ERC Consolidator Grant LEXICAL: Lexical Acquisition Across Languages (648909).
URL:
https://doi.org/10.17863/CAM.9718
https://www.repository.cam.ac.uk/handle/1810/294803
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10
Multi-Modal Representations for Improved Bilingual Lexicon Learning
Vulić, I
;
Kiela, D
;
Clark, Stephen
. - : Association for Computational Linguistics, 2016. : http://acl2016.org/, 2016. : Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, 2016
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