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CS298 ProposalAI Quantification of Language Puzzle to Language Learning GeneralizationHarita Shroff (harita.shroff@sjsu.edu) Advisor: Dr. Chris Pollett Committee Members: Kevin Smith, Younghee Park 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 sentence, filling in the blanks, providing flashcards and many more. Primarily this research focuses on quantifying the effectiveness of these games in learning a new language. Secondarily my goal for this project is to measure the effectiveness of exercises for transfer learning in machine translation. As part of CS297, I worked on projects involving different artificial intelligence topics. Mainly the focus was on different types of neural networks and their implementations. Language modeling and sequence-to-sequence learning were topics providing possible insight into the end goal of the project. CS297 Results
Proposed Schedule
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Innovations and Challenges
References:[2019] Introduction to Deep Learning. E. Charniak. Publisher. January 2019. [2015] A Survey of Machine Translation Techniques and Systems for Indian Languages. S. Saini and V. Sahula. IEEE International Conference on Computer Intelligence and Communication Technology. 2015. [2017] Replication Data for: A Trainable Spaced Repetition Model for Language Learning. Settles and Burr. Harvard Dataverse. 2017. [2018] Data for the 2018 Duolingo Shared Task on Second Language Acquisition Modeling (SLAM). Settles and Burr. Harvard Dataverse. 2018. [2015] Efficacy of New Language App. R. Vesselinov and J. Grego. NA. 2015 |