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QuACC : Question Answering for Cornell Courses 🦆

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Transfer Learning for QA Systems

Members: Kenta Takatsu (CS '19), Yuji Akimoto (CS '19), Chetan Velivela (CS '19)

Objective: To create a question answering system that can synthesize information from online resources such as textbooks and syllabi, and respond to questions about those resources. In the long term, we view this as an extremely useful tool for Cornell students that automatically answers easier questions asked on Piazza. In the short term, we will focus on creating a system to answer questions from the SQuAD dataset.

Sample QA

Model Architecture

R-Net

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