MorphoGen: Full Inflection Generation Using Recurrent Neural Networks

1. Aharoni, R., Goldberg, Y.: Morphological inflection generation with hard monotonic attention. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017, Vancouver, Canada, July 30 - August 4, Volume 1: Long Papers. pp. 2004–2015 (2017), http://doi.org/10.18653/v1/P17-1183

2. Ahlberg, M., Forsberg, M., Hulden, M.: Paradigm classification in supervised learning of morphology. In: Mihalcea, R., Chai, J.Y., Sarkar, A. (eds.) NAACL HLT 2015, The 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Denver, Colorado, USA, May 31 - June 5, 2015. pp. 1024–1029. The Association for Computational Linguistics (2015), http://aclweb.org/anthology/N/N15/N15-1107.pdf

3. Radford, A., Narasimhan, K., Salimans, T., Sutskever, I.: Improving language understanding by generative pre-training. http://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf

4. Radford, A., Narasimhan, K., Salimans, T., Sutskever, I.: Language models are unsupervised multitask learners. http://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf

5. Barbu, A.M.: Conjugarea verbelor româneşti. Dicţionar: 7500 de verbe româneşti grupate pe clase de conjugare. Bucharest: Coresi (2007), 4th edition, revised. (In Romanian.) (263 pp.)

   Google Scholar 

6. Barbu, A.M.: Romanian lexical databases: Inflected and syllabic forms dictionaries (2008)

   Google Scholar 

7. Cotterell, R., Kirov, C., Sylak-Glassman, J., Yarowsky, D., Eisner, J., Hulden, M.: The SIGMORPHON 2016 shared task–morphological reinflection. In: Proceedings of the 2016 Meeting of SIGMORPHON. Association for Computational Linguistics, Berlin, Germany (August 2016)

   Google Scholar 

8. Şulea, O.M.: Semi-supervised approach to romanian noun declension. In: Knowledge-Based and Intelligent Information Engineering Systems: Proceedings of the 20th International Conference KES-2016, York, UK, 5–7 September 2016. pp. 664–671 (2016), http://doi.org/10.1016/j.procs.2016.08.248

9. Dinu, L.P., Şulea, O.M., Niculae, V.: Sequence tagging for verb conjugation in romanian. In: RANLP. pp. 215–220 (2013)

   Google Scholar 

10. Dinu, L.P., Ionescu, E., Niculae, V., Şulea, O.M.: Can alternations be learned? A machine learning approach to verb alternations. In: Recent Advances in Natural Language Processing 2011. pp. 539–544 (September 2011)

    Google Scholar 

11. Dinu, L.P., Niculae, V., Şulea, O.M.: Learning how to conjugate the romanian verb. Rules for regular and partially irregular verbs. In: European Chapter of the Association for Computational Linguistics 2012. pp. 524–528 (April 2012)

    Google Scholar 

12. Durrett, G., DeNero, J.: Supervised learning of complete morphological paradigms. In: Vanderwende, L., III, H.D., Kirchhoff, K. (eds.) Human Language Technologies: Conference of the North American Chapter of the Association of Computational Linguistics, Proceedings, June 9–14, 2013, Westin Peachtree Plaza Hotel, Atlanta, Georgia, USA. pp. 1185–1195. The Association for Computational Linguistics (2013), http://aclweb.org/anthology/N/N13/N13-1138.pdf

13. Faruqui, M., Tsvetkov, Y., Neubig, G., Dyer, C.: Morphological inflection generation using character sequence to sequence learning. CoRR abs/1512.06110 (2015), http://arxiv.org/abs/1512.06110

14. Felbo, B., Mislove, A., Søgaard, A., Rahwan, I., Lehmann, S.: Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm. In: Conference on Empirical Methods in Natural Language Processing (EMNLP) (2017)

    Google Scholar 

15. Guţu-Romalo, V.: Morfologie Structurală a limbii române. Editura Academiei Republicii Socialiste România (1968), in Romanian

    Google Scholar 

16. Howard, J., Ruder, S.: Universal language model fine-tuning for text classification. In: ACL. Association for Computational Linguistics (2018), http://arxiv.org/abs/1801.06146

17. Woolf, M.: textgenrnn, http://github.com/minimaxir/textgenrnn

18. Zhou, C., Neubig, G.: Morphological inflection generation with multi-space variational encoder-decoders. In: Proceedings of the CoNLL SIGMORPHON 2017 Shared Task: Universal Morphological Reinflection, Vancouver, BC, Canada, August 3–4, 2017. pp. 58–65 (2017), http://doi.org/10.18653/v1/K17-2005

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