Publication Date

Spring 2020

Degree Type

Master's Project

Degree Name

Master of Science (MS)

Department

Computer Science

First Advisor

Chris Pollett

Second Advisor

Katerina Potika

Third Advisor

Kevin Smith

Keywords

Recurrent Neural Network (RNN), Deep Learning, Sequence-to-sequence Learning, Machine Translation.

Abstract

Online language learning applications provide users multiple ways/games to learn a new language. Some of the ways include rearranging words in the foreign language sentences, filling in the blanks, providing flashcards, and many more. Primarily this research focused on quantifying the effectiveness of these games in learning a new language. Secondarily my goal for this project was to measure the effectiveness of exercises for transfer learning in machine translation. Currently, very little research has been done in this field except for the research conducted by the online platforms to provide assurance to their users [12]. Machine learning has been used in this research to achieve the goals mentioned earlier. Specifically, deep learning models with Recurrent Neural Network (RNN) were employed to process the data. Models were designed on popular exercises from these platforms using sequence-to-sequence learning. Our research discovered that most of the models had cross-validation accuracy in the range of 70%- 80%. This result shows that knowledge learned from one model is transferrable to the other.

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