Ion Madrazo Azpiazu and Maria Soledad Pera. 2020. “An Analysis of Transfer Learning Methods for Multilingual Readability Assessment”. Poster paper in Proceedings of the 2020 Conference on User Modeling, Adaptation and Personalization (UMAP ‘20). ACM, 95-100 pp. DOI:10.1145/3386392.3397605.
Recent advances in readability assessment have lead to the introduction of multilingual strategies that can predict the reading-level of a text regardless of its language. These strategies, however, tend to be limited to just operating in different languages rather than taking any explicit advantage of the multilingual corpora they utilize. In this manuscript, we discuss the results of the in-depth empirical analysis we conducted to assess the language transfer capabilities of four different strategies for readability assessment with increasing multilingual power. Results showcase that transfer learning is a valid option for improving the performance of readability assessment, particularly in the case of typologically-similar languages and when training corpora availability is limited.