The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Learning principled bilingual mappings of word embeddings while preserving monolingual invariance
Open accessAbstract
Mapping word embeddings of different languages into a single space has multiple applications.In order to map from a source space into a target space, a common approach is to learn a linear mapping that minimizes the distances between equivalences listed in a bilingual dictionary.In this paper, we propose a framework that generalizes previous work, provides an efficient exact method to learn the optimal linear transformation and yields the best bilingual results in translation induction while preserving monolingual performance in an analogy task.
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Cites
Efficient Estimation of Word Representations in Vector Space
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1301.3781 · Cited by 26,568
Exploiting Similarities among Languages for Machine Translation
Mikolov, T., Le, Q. V., & Sutskever, I. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1309.4168 · Cited by 1,346
Improving Vector Space Word Representations Using Multilingual Correlation
Faruqui, M., & Dyer, C. (2014) · Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics · doi:10.3115/v1/e14-1049 · Cited by 620
Bilingual Word Embeddings for Phrase-Based Machine Translation
Zou, W. Y., Socher, R., Cer, D., & Manning, C. D. (2013) · Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d13-1141 · Cited by 518
Normalized Word Embedding and Orthogonal Transform for Bilingual Word Translation
Xing, C., Wang, D., Liu, C., & Lin, Y. (2015) · Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.3115/v1/n15-1104 · Cited by 381
Bilingual Word Representations with Monolingual Quality in Mind
Luong, T., Pham, H., & Manning, C. D. (2015) · Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing · doi:10.3115/v1/w15-1521 · Cited by 315
Improving zero-shot learning by mitigating the hubness problem
Dinu, G., Lazaridou, A., & Baroni, M. (2015) · Figshare · doi:10.5281/zenodo.2654863 · Cited by 310
An Autoencoder Approach to Learning Bilingual Word Representations
P, S. C. A., Lauly, S., Larochelle, H., Khapra, M. M., Ravindran, B., Raykar, V. C., & Saha, A. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1402.1454 · Cited by 309
Hubness and Pollution: Delving into Cross-Space Mapping for Zero-Shot Learning
Lazaridou, A., Dinu, G., & Baroni, M. (2015) · Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.3115/v1/p15-1027 · Cited by 210
Deep Multilingual Correlation for Improved Word Embeddings
Lu, A., Wang, W., Bansal, M., Gimpel, K., & Livescu, K. (2015) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.3115/v1/n15-1028 · Cited by 134
Cited by
Unsupervised Neural Machine Translation
Artetxe, M., Labaka, G., Agirre, E., & Cho, K. (2017) · arXiv · doi:10.48550/arxiv.1710.11041 · Cited by 632
Self-citationA robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings
Artetxe, M., Labaka, G., & Agirre, E. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1073 · Cited by 555
Self-citationLearning bilingual word embeddings with (almost) no bilingual data
Artetxe, M., Labaka, G., & Agirre, E. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1042 · Cited by 455
Self-citationFrom Word To Sense Embeddings: A Survey on Vector Representations of Meaning
Camacho-Collados, J., & Pilehvar, M. T. (2018) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.11259 · Cited by 363
A Survey of Cross-lingual Word Embedding Models
Ruder, S., Vulić, I., & Søgaard, A. (2019) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.11640 · Cited by 266
Metadata
- Authors
- Artetxe, M., Labaka, G., & Agirre, E.
- Venue
- Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing
- Published
- 2016
- Pages
- 2289-2294
- OpenAlex
- W2561995736
- Cited by
- 369
- References
- 15
- FWCI
- 26.14
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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