Recovering Word Forms by Context for Morphologically Rich Languages

1. I. Anisimov, V. Polyakov, E. Makarova, and V. Solovyev, “Spelling correction in english: Joint use of bi-grams and chunking,” in: 2017 Intelligent Systems Conference (IntelliSys), IEEE (2017), pp. 886–892.

2. D. Bahdanau, K. Cho, and Y. Bengio, “Neural machine translation by jointly learning to align and translate,” arXiv preprint arXiv:1409.0473 (2014).

3. D. Gavrilov, P. Kalaidin, and V. Malykh, “Self-attentive model for headline generation,” CoRR abs/1901.07786 arXiv:1901.07786 (2019).

4. S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Comput., 9, No. 8, 1735–1780 (1997).

   Article  Google Scholar 

5. G. Klein, Y. Kim, Y. Deng, J. Senellart, and A. M. Rush, “OpenNMT: Open-Source Toolkit for Neural Machine Translation,” ArXiv e-prints arXiv:1701.02810 (2017). https://arxiv.org/abs/1701.02810

6. Koehn, P., H. Hoang, A. Birch, Chr. Callison-Burch, M. Federico, N. Bertoldi, B. Cowan, W. Shen, Chr. Moran, Zens R., et al., “Moses: Open source toolkit for statistical machine translation,” in:Proceedings of the 45th annual meeting of the ACL on interactive poster and demonstration sessions, Association for Computational Linguistics (2007), pp. 177– 180.

7. M. Korobov, “Morphological analyzer and generator for russian and ukrainian languages,” in: Analysis of Images, Social Networks and Texts (M. Yu. Khachay, N. Konstantinova, A. Panchenko, D.I. Ignatov, and V.G. Labunets, eds.), Communications in Computer and Information Science, Vol. 542, Springer International Publishing (2015), pp. 320–332.

8. J. Lee, K. Cho, and Th. Hofmann, “Fully character-level neural machine translation without explicit segmentation,” Transactions of the Association for Computational Linguistics 5, 365–378 (2017).

   Article  Google Scholar 

9. M.-Th. Luong, H. Pham, and Chr. D. Manning, “Effective approaches to attention-based neural machine translation,” arXiv preprint arXiv:1508.04025 (2015).

10. Z. Miftahutdinov and E. Tutubalina, “Deep learning for ICD coding: Looking for medical concepts in clinical documents in english and in french,” in: Experimental IR Meets Multilinguality, Multimodality, and Interaction (Cham) (P. Bellot, Ch. Trabelsi, J. Mothe, F. Murtagh, J. Y. Nie, L. Soulier, E. SanJuan, L. Cappellato, and N. Ferro, eds.), Springer International Publishing, 2018, pp. 203–215.

11. A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, Automatic differentiation in PyTorch, NIPS-W, 2017.

    Google Scholar 

12. D. Polykovskiy, D. Soloviev, and S. Nikolenko, “Concorde: Morphological agreement in conversational models,” in: Proceedings of The 10th Asian Conference on Machine Learning (J. Zhu and I. Takeuchi, eds.), Proceedings of Machine Learning Research, Vol. 95, PMLR (2018), pp. 407–421.

13. I. Segalovich, A Fast Morphological Algorithm with Unknown Word Guessing Induced by a Dictionary for a Web Search Engine, MLMTA, Citeseer (2003), pp. 273–280.

    Google Scholar 

14. D. Sukhonin and A. Panchenko, A Python wrapper of the Tandex mystem 3.1 morphological analyzer, https://github.com/nlpub/pymystem3 (2013).

15. A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, and I. Polosukhin, Attention is all you need, arXiv:1706.03762 (2017).

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