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A study of universal morphological analysis using morpheme-based, holistic, and neural approaches under various data size conditions

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Abstract

We perform a study on the universal morphological analysis task: given a word form, generate the lemma (lemmatisation) and its corresponding morphosyntactic descriptions (MSD analysis). Experiments are carried out on the SIGMORPHON 2018 Shared Task: Morphological Reinflection Task dataset which consists of more than 100 different languages with various morphological richness under three different data size conditions: low, medium and high. We consider three main approaches: morpheme-based (eager learning), holistic (lazy learning), and neural (eager learning). Performance is evaluated on the two subtasks of lemmatisation and MSD analysis. For the lemmatisation subtask, under all three data sizes, experimental results show that the holistic approach predicted more accurate lemmata, while the morpheme-based approach produced lemmata closer to the answers when it produces the wrong answers. For the MSD analysis subtask, under all three data sizes, the holistic approach achieves higher recall, while the morpheme-based approach is more precise. However, the trade-off between precision and recall of the two systems leads to a very similar overall F1 score. On the whole, neural approaches suffer under low resource conditions, but they achieve the best performance in comparison to the other approaches when the size of the training data increases.

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The data used in this research are all publicly available for download through the links provided in the body of the paper.

References

  1. Bender, E.: The #BenderRule: On naming the languages we study and why it matters. The Gradient (2019)

  2. Bickel, B., Nichols, J.: Inflectional synthesis of the verb (v2020.3). In: Dryer, M.S., Haspelmath, M. (eds.) The World Atlas of Language Structures Online. Zenodo, (Available online at https://wals.info) (2013). https://doi.org/10.5281/zenodo.7385533

  3. Fam, R., Lepage, Y.: Morphological predictability of unseen words using computational analogy. In: Proceedings of the Computational Analogy Workshop at the 24th International Conference on Case-Based Reasoning (ICCBR-CA-2016), Atlanta, Georgia, pp. 51–60 (2016)

  4. Singh, R., Ford, A.: In praise of Sakatayana: some remarks on whole word morphology. In: Singh, R. (ed.) The Yearbook of South Asian Languages and Linguistics-200. Sage, Thousand Oaks (2000)

    Chapter  Google Scholar 

  5. Neuvel, S., Fulop, S.A.: Unsupervised learning of morphology without morphemes. In: Proceedings of the ACL-02 Workshop on Morphological and Phonological Learning, pp. 31–40. Assoc. Comput. Linguist., Philadelphia (2002). https://doi.org/10.3115/1118647.1118651

  6. Hathout, N.: Acquistion of the morphological structure of the lexicon based on lexical similarity and formal analogy. In: Proceedings of the 3rd Textgraphs Workshop on Graph-based Algorithms for Natural Language Processing, pp. 1–8. Coling 2008 Organizing Committee, Manchester, UK (2008)

  7. Chan, E.: Structures and distributions in morphology learning. PhD thesis, University of Pennsylvania. (2008)

  8. Fam, R., Lepage, Y.: Organising lexica into analogical grids: A study of a holistic approach for morphological generation under various sizes of data in various languages. J. Experiment. Theor. Artif. Intell. , 1–26 (2022). https://doi.org/10.1080/0952813X.2022.2078890

  9. Kirov, C., Cotterell, R., Sylak-Glassman, J., Walther, G., Vylomova, E., Xia, P., Faruqui, M., Mielke, S., McCarthy, A.D., Kübler, S., Yarowsky, D., Eisner, J., Hulden, M.: UniMorph 2.0: Universal Morphology. In: chair), N.C.C., Choukri, K., Cieri, C., Declerck, T., Goggi, S., Hasida, K., Isahara, H., Maegaard, B., Mariani, J., Mazo, H., Moreno, A., Odijk, J., Piperidis, S., Tokunaga, T. (eds.) Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC-2018). European Language Resources Association (ELRA), Miyazaki, Japan (2018)

  10. Cotterell, R., Kirov, C., Sylak-Glassman, J., Walther, G., Vylomova, E., McCarthy, A.D., Kann, K., Mielke, S., Nicolai, G., Silfverberg, M., Yarowsky, D., Eisner, J., Hulden, M.: The CoNLL–SIGMORPHON 2018 shared task: Universal morphological reinflection. In: Proceedings of the CoNLL SIGMORPHON 2018 Shared Task: Universal Morphological Reinflection, pp. 1–27. Assoc. Comput. Linguist., Brussels (2018)

  11. Singh, R. (ed.): The Yearbook of South Asian Languages and Linguistics-200. Sage, Thousand Oaks (2000)

    Google Scholar 

  12. Neuvel, S., Singh, R.: Vive la différence ! what morphology is about. Folia Linguistica 35(3–4), 313–320 (2001)

    Google Scholar 

  13. Marquer, E., Alsaidi, S., Decker, A., Murena, P.-A., Couceiro, M.: A deep learning approach to solving morphological analogies. In: Keane, M.T., Wiratunga, N. (eds.) Case-Based Reasoning Research and Development, pp. 159–174. Springer, Cham (2022)

    Chapter  Google Scholar 

  14. Chan, K., Kaszefski-Yaschuk, S.P., Saran, C., Marquer, E., Couceiro, M.: Solving morphological analogies through generation. In: Couceiro, M., Murena, P. (eds.) Proceedings of the Workshop on the Interactions Between Analogical Reasoning and Machine Learning (International Joint Conference on Artificial Intelligence - European Conference on Artificial Intelligence (IJAI-ECAI 2022)), Vienna, Austria, July 23, 2022. CEUR Workshop Proceedings, vol. 3174, pp. 29–39. CEUR-WS.org, Vienna, Austria (2022)

  15. Murena, P.-A., Al-Ghossein, M., Dessalles, J.-L., Cornuéjols, A.: Solving analogies on words based on minimal complexity transformation. In: Bessiere, C. (ed.) Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI-20, pp. 1848–1854. International Joint Conferences on Artificial Intelligence Organization, Yokohama, Japan (2020) Main track. https://doi.org/10.24963/ijcai.2020/256

  16. Fam, R., Lepage, Y.: Tools for The Production of Analogical Grids and a Resource of N-gram Analogical Grids in 11 Languages. In: chair), N.C.C., Choukri, K., Cieri, C., Declerck, T., Goggi, S., Hasida, K., Isahara, H., Maegaard, B., Mariani, J., Mazo, H., Moreno, A., Odijk, J., Piperidis, S., Tokunaga, T. (eds.) Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC-2018). European Language Resources Association (ELRA), Miyazaki, Japan (2018)

  17. Lepage, Y.: Solving analogies on words: an algorithm. In: Proceedings of the 17th International Conference on Computational Linguistics (COLING-1998). Assoc. Comput. Linguist. vol. 1, pp. 728–734 (1998)

  18. Yvon, F.: Finite-state machines solving analogies on words. Technical report, ENST (2003)

    Google Scholar 

  19. Langlais, P., Patry, A.: Translating unknown words by analogical learning. In: Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), pp. 877–886 (2007). https://www.aclweb.org/anthology/D/D07/D07-1092.pdf

  20. Luong, T., Pham, H., Manning, C.D.: Effective approaches to attention-based neural machine translation. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1412–1421. Assoc. Comput. Linguist., Lisbon, Portugal (2015)

  21. Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning phrase representations using rnn encoder–decoder for statistical machine translation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP-2014), pp. 1724–1734. Assoc. Comput. Linguist., Doha, Qatar (2014)

  22. Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. In: Proceedings of the 3rd International Conference for Learning Representations (ICLR-15), San Diego (2015)

  23. Salimans, T., Kingma, D.P.: Weight normalization: A simple reparameterization to accelerate training of deep neural networks. In: Lee, D.D., Sugiyama, M., Luxburg, U.V., Guyon, I., Garnett, R. (eds.) Advances in Neural Information Processing Systems 29, pp. 901–909. Curran Associates Inc, Red Hook (2016)

    Google Scholar 

  24. Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: Proceedings of the 3rd International Conference for Learning Representations (ICLR-2015), San Diego (2015)

  25. Levenshtein, V.I.: Binary codes capable of correcting deletions, insertions and reversals. Soviet Physics-doklady 10(8), 707–710 (1966)

    MathSciNet  Google Scholar 

  26. Wagner, R.A., Fischer, M.J.: The string-to-string correction problem. J. Assoc. Comput. Mach. 21(1), 168–173 (1974)

    Article  MathSciNet  Google Scholar 

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Funding

This work was supported by a JSPS grant, number 21K12038 (Kakenhi Kiban C), entitled “Theoretically founded algorithms for the automatic production of analogy tests in NLP”.

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  1. Yves Lepage contributed equally to this work.

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  1. Graduate School of Information, Production, and Systems, Waseda University, 2-7 Hibikino, Wakamatsu-ku, Kitakyushu-shi, 808-0135, Fukuoka-ken, Japan

    Rashel Fam & Yves Lepage

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  1. Rashel Fam
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  2. Yves Lepage
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Correspondence to Rashel Fam.

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Fam, R., Lepage, Y. A study of universal morphological analysis using morpheme-based, holistic, and neural approaches under various data size conditions. Ann Math Artif Intell (2024). https://doi.org/10.1007/s10472-024-09944-8

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