The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings
Open accessAbstract
Recent work has managed to learn crosslingual word embeddings without parallel data by mapping monolingual embeddings to a shared space through adversarial training.However, their evaluation has focused on favorable conditions, using comparable corpora or closely-related languages, and we show that they often fail in more realistic scenarios.This work proposes an alternative approach based on a fully unsupervised initialization that explicitly exploits the structural similarity of the embeddings, and a robust self-learning algorithm that iteratively improves this solution.Our method succeeds in all tested scenarios and obtains the best published results in standard datasets, even surpassing previous supervised systems.Our implementation is released as an open source project at https://github. com/artetxem/vecmap.
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Cites
Parallel Data, Tools and Interfaces in OPUS
Tiedemann, J. (2012) · Language Resources and Evaluation · Cited by 1,421
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
Word translation without parallel data
Lample, G., Conneau, A., Ranzato, M., Denoyer, L., & Jeǵou, H. (2018) · International Conference on Learning Representations · Cited by 653
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
Learning 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-citationNormalized 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
Learning principled bilingual mappings of word embeddings while preserving monolingual invariance
Artetxe, M., Labaka, G., & Agirre, E. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1250 · Cited by 369
Self-citationImproving zero-shot learning by mitigating the hubness problem
Dinu, G., Lazaridou, A., & Baroni, M. (2015) · Figshare · doi:10.5281/zenodo.2654863 · Cited by 310
Artetxe, M., Labaka, G., & Agirre, E. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11992 · Cited by 220
Self-citationHubness 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
Cited by
Self-supervised speech representation learning: A review
Mohamed, A., Lee, H.-Y., Borgholt, L., Havtorn, J. D., Edin, J., Igel, C., Kirchhoff, K., Li, S.-W., Livescu, K., Maaløe, L., et al. (2022) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2022.3207050 · Cited by 372
“[271] Speech2vec (English) Oracle Word2Vec (French) VecMap [272] LM rescore, sequence DAE ST (BLEU): 10.”
BackgroundFrom 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
Extending Multilingual BERT to Low-Resource Languages
Wang, Z., K, K., Mayhew, S., & Roth, D. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020 · doi:10.18653/v1/2020.findings-emnlp.240 · Cited by 90
A Systematic Review of Reproducibility Research in Natural Language Processing
Belz, A., Agarwal, S., Shimorina, A., & Reiter, E. (2021) · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume · doi:10.18653/v1/2021.eacl-main.29 · Cited by 86
Metadata
- Authors
- Artetxe, M., Labaka, G., & Agirre, E.
- Venue
- Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2018
- Pages
- 789-798
- OpenAlex
- W2798908575
- Cited by
- 555
- References
- 14
- FWCI
- 38.49
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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- Artetxe, M., Labaka, G., & Agirre, E. (2018). A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). https://doi.org/10.18653/v1/p18-1073
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