IGARRITZ: euskarazko testu-iragarpenerako web-ingurune egokitua

Abstract

Students with limited mobility, for example, those caused by brain paralysis, have adapted tools for writing texts, such as eye-tracking hardware, to select letters and predict words. For instance, they can use eye-tracking hardware to select letters and choose words predicted by the system. These systems offer resources for writing in Basque, and predictions can be customized by inputting Basque word lists. The primary aim of text prediction is to alleviate the effort involved in typing and to facilitate faster or increased text production. However, writing with Iris is slower and more challenging compared to conventional typing with ten fingers. Furthermore, predictive text functionality in Basque is comparatively less effective than in other languages, offering minimal quality output. Thus, the objective of this study is to develop an adapted web environment for Basque text prediction employing artificial intelligence techniques. To achieve this goal, we have developed a web interface named IGARRITZ based on the HiTZ/roberta-eus-euscrawl-base-cased language model, utilizing a Transformer architecture. It was re-trained with an educational Basque corpus sourced from educational texts from Gizapedia, Wikipedia, and Berria. We evaluated the performance of the created text predictor by comparing it with an existing system, using texts produced by a secondary school student. The results indicate that IGARRITZ enhances text prediction in Basque, as reported by the user who employs Iris, expressing that writing becomes notably easier and more efficient. Additionally, we conducted an automatic evaluation, which also yielded superior results compared to the existing system.