Advisor: Dr. Chris Pollett

Description:

Question Answering(QA) is about making a computer program that could answer questions in natural language automatically. QA techniques are widely used among search engines, personal assistant applications on smart phones, voice control systems and a lot more other applications. In recent years, more end-to-end neural network architectures are built to do question answering tasks. In contrast, traditional solutions use syntactic and semantic analyses and hand made features. End-to-end neural network approach gives more accurate result. However, traditional ways are more explainable. The Stanford Question Answering Dataset (SQuAD)[1] is used in this project. It includes questions asked by human beings on Wikipedia articles. The answer to each question is a segment of the corresponding Wikipedia article[1]. In total, SQuAD contains 100,000+ question-answer pairs on 500+ articles[1]. The goal of this project is to build a QA system on SQuAD using an end-to-end neural network architecture.

Schedule:

Deliverables:

The full project will be done when CS298 is completed. The following will be done by the end of CS297:

1. Calculation of Back Propagation

2. Implementation of Word Embedding

3. Setting Up Development Infrastructure and Downloading Data

4. Implementation of the neural network model in [2]

5. CS297 report.

References:

1. https://rajpurkar.github.io/SQuAD-explorer/
2. Wang, Shuohang, and Jing Jiang. "Machine comprehension using match-lstm and answer pointer." arXiv preprint arXiv:1608.07905 (2016).