1. Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. CoRR abs/1607.04606 (2016).http://arxiv.org/abs/1607.04606

2. Brunato, D., De Mattei, L., Dell’Orletta, F., Iavarone, B., Venturi, G.: Is this sentence difficult? Do you agree? In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, pp. 2690–2699. Association for Computational Linguistics (2018).https://doi.org/10.18653/v1/D18-1289,https://aclanthology.org/D18-1289

3. Brysbaert, M., New, B.: Subtlex-us frequency list with POS information final text version (2013)

   Google Scholar 

4. Collins-Thompson, K., Callan, J.: Predicting reading difficulty with statistical language models. J. Am. Soc. Inform. Sci. Technol.56(13), 1448–1462 (2005)

   Article  Google Scholar 

5. Crossley, S.A., Greenfield, J., McNamara, D.S.: Assessing text readability using cognitively based indices. TESOL Q.42(3), 475–493 (2008)

   Article  Google Scholar 

6. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprintarXiv:1810.04805 (2018)

7. Heilman, M., Collins-Thompson, K., Eskenazi, M.: An analysis of statistical models and features for reading difficulty prediction. In: Proceedings of the Third Workshop on Innovative Use of NLP for Building Educational Applications, pp. 71–79 (2008)

   Google Scholar 

8. Iavarone, B., Brunato, D., Dell’Orletta, F.: Sentence complexity in context. In: Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics, pp. 186–199. Association for Computational Linguistics, Online (2021).https://doi.org/10.18653/v1/2021.cmcl-1.23

9. Inui, K., Yamamoto, S.: Corpus-based acquisition of sentence readability ranking models for deaf people. In: Proceedings of the Sixth Natural Language Processing Pacific Rim Symposium, 27–30 November 2001, Tokyo, Japan, pp. 159–166, Hitotsubashi Memorial Hall, National Center of Sciences (2001).http://www.afnlp.org/nlprs2001/pdf/0035-01.pdf

10. Ivanov, V.: Sentence-level complexity in Russian: an evaluation of BERT and graph neural networks. Front. Artif. Intell.5 (2022)

    Google Scholar 

11. Ivanov, V., Elbayoumi, M.G.: A new dataset for sentence-level complexity in Russian. In: Computational Linguistics and Intellectual Technologies: Papers from the Annual International Conference “Dialogue” (2023)

    Google Scholar 

12. Khallaf, N., Sharoff, S.: Automatic difficulty classification of Arabic sentences. In: Workshop on Arabic Natural Language Processing (2021)

    Google Scholar 

13. Lo Bosco, G., Pilato, G., Schicchi, D.: Deepeva: a deep neural network architecture for assessing sentence complexity in Italian and English languages. Array12, 100097 (2021)https://doi.org/10.1016/j.array.2021.100097,https://www.sciencedirect.com/science/article/pii/S2590005621000424

14. Lyashevskaya, O., Sharov, S.A.: Frequency dictionary of the modern Russian language (the Russian National Corpus) (2009)

    Google Scholar 

15. Lyding, V., et al.: The paisa’ corpus of Italian web texts (2014).https://doi.org/10.3115/v1/W14-0406

16. Maqsood, S., et al.: Assessing English language sentences readability using machine learning models. PeerJ Comput. Sci.7, e818 (2022)

    Article  Google Scholar 

17. Schicchi, D., Pilato, G., Bosco, G.L.: Deep neural attention-based model for the evaluation of Italian sentences complexity. In: 2020 IEEE 14th International Conference on Semantic Computing (ICSC), pp. 253–256. IEEE (2020)

    Google Scholar 

18. Schumacher, E., Eskenazi, M., Frishkoff, G., Collins-Thompson, K.: Predicting the relative difficulty of single sentences with and without surrounding context. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 1871–1881, Austin, Texas. Association for Computational Linguistics (2016).https://doi.org/10.18653/v1/D16-1192,https://aclanthology.org/D16-1192

19. Shardlow, M., Cooper, M., Zampieri, M.: CompLex — a new corpus for lexical complexity prediction from Likert Scale data. In: Proceedings of the 1st Workshop on Tools and Resources to Empower People with REAding DIfficulties (READI), Marseille, France, pp. 57–62. European Language Resources Association (2020).https://aclanthology.org/2020.readi-1.9

20. Shardlow, M., Evans, R., Paetzold, G.H., Zampieri, M.: SemEval-2021 task 1: lexical complexity prediction. In: Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021). pp. 1–16. Association for Computational Linguistics, Online (2021).https://doi.org/10.18653/v1/2021.semeval-1.1,https://aclanthology.org/2021.semeval-1.1

21. Vajjala, S., Meurers, D.: Assessing the relative reading level of sentence pairs for text simplification. In: Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, Gothenburg, Sweden, pp. 288–297. Association for Computational Linguistics (2014).https://doi.org/10.3115/v1/E14-1031,https://aclanthology.org/E14-1031

22. Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)

    Google Scholar 

Download references