Gamifying Machine Teaching: Human-in-the-Loop Approach for Diphthong and Hiatus Identification in Spanish Language

Abstract

Human-in-the-Loop Machine Learning (HITL-ML) is a set of techniques that attempt to actively involve experts into the learning loop of machine learning (ML) models. One of these techniques is Machine Teaching (MT) which tries to apply techniques that come from the world of didactics within machine learning (ML), such as sorting the dataset according to its difficulty and presenting the cases to the model in incremental levels of complexity. In this work we propose a new twist to MT: since its foundation is to bring didactic techniques to ML, why not use this technique as a didactic method itself? In this case we propose the creation of an ML model for the identification of diphthongs and hiatuses in the Spanish language. The first step is to develop a deep learning model to identify diphthongs and hiatuses using Curriculum Learning (CL) and a sorted dataset that identifies simple and complex cases. The accuracy of this model identifies the upper limit of efficiency that we can obtain by training the model. The next step is to reset the weights of the model but retain its architecture and offer the model to the students for its training. The idea is that students use MT techniques to make the model learn again, but the ultimate goal is that students learn by teaching in an informal and gamified learning environment. The results show how a HITL strategy can make a model learn iteratively to identify diphthongs and hiatuses and a workflow is proposed to include this technique in the classroom.