Semantic Similarity Between Medium-Sized Texts

Semantically comparing texts is a task that is useful in various fields, such as the automatic correction of exams and/or activities. Making use of Natural Language Processing (NLP) and deep learning techniques, the correction task can be facilitated for the teacher, so that a greater number of knowledge tests can be offered to the student. The objective of this work is to semantically compare texts in order to be able to evaluate the student’s knowledge automatically. For this, models will be built based on Transformers architectures, specialized in the Spanish language and in 2 subjects. These models can be used and evaluated through an application. After using the different models to measure the similarity between a set of student’s answers and the ideal answer provided by the teacher, a Pearson correlation coefficient greater than 80% is obtained when comparing the similarity measured and the teacher’s grade. Given the Pearson correlation obtained, a MAE of 0.13 and an RMSE of 0.17, it is concluded that the models obtained would serve as an evaluation guide for both the teaching team and the students in the medium term, opening the door to further research to create an autonomous system in the long-term.