Low Resource Machine Translation

1. Alsulami A (2019) A sociolinguistic analysis of the use of Arabizi in social media among Saudi Arabians. Int J Engl Linguis 9:257. https://doi.org/10.5539/ijel.v9n6p257

   Article  Google Scholar 

2. Artetxe M, Labaka G, Agirre E (2017) Learning bilingual word embeddings with (almost) no bilingual data. In: ACL

   Google Scholar 

3. Conneau A, Lample G, Ranzato M, Denoyer L, Jégou H (2017) Word translation without parallel data. arXiv preprint arXiv:171004087

4. Edunov S, Ott M, Auli M, Grangier D (2018) Understanding back-translation at scale. In: Proceedings of the 2018 conference on empirical methods in natural language processing, Association for Computational Linguistics, Brussels, pp 489–500. https://doi.org/10.18653/v1/D18-1045. https://aclanthology.org/D18-1045

5. Garcia X, Siddhant A, Firat O, Parikh A (2021) Harnessing multilinguality in unsupervised machine translation for rare languages. In: Proceedings of the 2021 conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, Online, pp 1126–1137. https://doi.org/10.18653/v1/2021.naacl-main.89. https://aclanthology.org/2021.naacl-main.89

6. Guzmán F, Chen PJ, Ott M, Pino J, Lample G, Koehn P, Chaudhary V, Ranzato M (2019) The FLORES evaluation datasets for low-resource machine translation: Nepali–English and Sinhala–English. In: Proceedings of the 2019 conference on Empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP), Association for Computational Linguistics, Hong Kong, China, pp 6098–6111. https://doi.org/10.18653/v1/D19-1632. https://aclanthology.org/D19-1632

7. Karras T, Aittala M, Hellsten J, Laine S, Lehtinen J, Aila T (2020) Training generative adversarial networks with limited data. In: Larochelle H, Ranzato M, Hadsell R, Balcan MF, Lin H (eds) Advances in neural information processing systems. Curran Associates, Inc., vol 33, pp 12104–12114. https://proceedings.neurips.cc/paper/2020/file/8d30aa96e72440759f74bd2306c1fa3d-Paper.pdf

8. KhudaBukhsh AR, Palakodety S, Mitchell TM (2020) Discovering bilingual lexicons in polyglot word embeddings. CoRR abs/2008.13347, https://arxiv.org/abs/2008.13347, 2008.13347

9. Lample G, Ott M, Conneau A, Denoyer L, Ranzato M (2018) Phrase-based & neural unsupervised machine translation. In: Proceedings of the 2018 conference on empirical methods in natural language processing. Association for Computational Linguistics, Brussels, pp 5039–5049. https://doi.org/10.18653/v1/D18-1549. https://aclanthology.org/D18-1549

10. Liu Y, Gu J, Goyal N, Li X, Edunov S, Ghazvininejad M, Lewis M, Zettlemoyer L (2020) Multilingual denoising pre-training for neural machine translation. Trans Associ Comput Linguist 8:726–742

    Article  Google Scholar 

11. Marchisio K, Duh K, Koehn P (2020) When does unsupervised machine translation work? In: Proceedings of the fifth conference on machine translation. Association for Computational Linguistics, Online, pp 571–583. https://aclanthology.org/2020.wmt-1.68

12. Mikolov T, Le QV, Sutskever I (2013) Exploiting similarities among languages for machine translation. ArXiv abs/1309.4168

    Google Scholar 

13. Palakodety S, KhudaBukhsh AR, Carbonell JG (2020) Hope speech detection: a computational analysis of the voice of peace. In: Giacomo GD, Catalá A, Dilkina B, Milano M, Barro S, Bugarín A, Lang J (eds) ECAI 2020—24th European conference on artificial intelligence. Frontiers in artificial intelligence and applications. IOS Press, vol 325, pp 1881–1889. https://doi.org/10.3233/FAIA200305. https://doi.org/10.3233/FAIA200305

14. Sennrich R, Haddow B, Birch A (2016) Improving neural machine translation models with monolingual data. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Association for Computational Linguistics, Berlin, pp 86–96. https://doi.org/10.18653/v1/P16-1009. https://aclanthology.org/P16-1009

15. Stahlberg F (2019) Neural machine translation: a review and survey. arXiv: Computation and Language

16. Takase S, Kiyono S (2021a) Lessons on parameter sharing across layers in transformers. ArXiv abs/2104.06022

    Google Scholar 

17. Takase S, Kiyono S (2021b) Rethinking perturbations in encoder-decoders for fast training. In: Proceedings of the 2021 conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, Online, pp 5767–5780. https://doi.org/10.18653/v1/2021.naacl-main.460. https://aclanthology.org/2021.naacl-main.460

18. Vincent P, Larochelle H, Bengio Y, Manzagol PA (2008) Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th international conference on Machine learning, pp 1096–1103

    Google Scholar 

19. Vulić I, Glavaš G, Reichart R, Korhonen A (2019) Do we really need fully unsupervised cross-lingual embeddings? In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, pp 4407–4418. https://doi.org/10.18653/v1/D19-1449. https://aclanthology.org/D19-1449

20. WMT20 (2021) Fifth conference on machine translation (wmt20). https://www.statmt.org/wmt20/

21. WMT21 (2021) Sixth conference on machine translation (wmt21). https://www.statmt.org/wmt21/

22. Zhang Y (2017) The semiotic multifunctionality of Arabic numerals in Chinese online discourse. Language@Internet 14

    Google Scholar 

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