Text Prediction using Machine Learning

Sammanfattning

Language modeling is a very broad field and has been used for various purposes for a long period of time to make the lives of people easier. Language modeling is also used for text prediction for mobile keyboards to make the user experience smooth. Tobii has been working since 2001 for users who are suffering from ALS (Amyotrophic Lateral Sclerosis). In this disease, users are unable to talk, walk or chew due to the weakening of voluntary muscles and this gets worse day by day. Tobii has designed an Eye Tracker solution for people suffering from ALS to do their tasks more conveniently. They also developed a keyboard for talking which is controlled by an Eye Tracker device. Users can write sentences using the keyboard and then convey them to other people by conversion of this keyboard written text to speech. Therefore, the thesis is related to predicting the text on the initial input of the keyboard to make the user experience fast, easy and less hectic. This thesis project was conducted at Tobii Dynavox with the objective to build a language model which is an automatic, fast, and efficient approach to predict the text for the given input of text. It explores the way to predict sentences by using deep learning models on the initial text input from users and predict the text by taking into consideration user-specific writing style. The model developed in the thesis could be used by Tobii Dynavox for the end-users to predict the text. Part of the objective is also to find out which is the better approach for the implementation of the language models. The results show that federated learning is performing better than centralized machine learning. After analysing the results, it can also be said that Gated Recurrent Units (GRU) will be a good choice for our models because these models show better results for accuracy and take less training and response times.