Rewritten on Nov 14, 2017 Due to Change of Topic
CS297 Proposal
A Question Answering System on SQuAD Dataset Using an End-to-end Neural Network
Bo Li (bo.nov29@gmail.com)
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:
Week 1 - 4:
08/20 - 09/24 | Deliverable #1 : Calculation of Back Propagation |
Week 5 - 11:
09/25 - 11/12 | Deliverable #2 : Implementation of Word Embedding |
Week 12:
11/13 - 11/19 | Deliverable #3 : Setting Up Development Infrastructure and Downloading Data |
Week 13 - 14:
11/20 - 12/03 | Deliverable #4 : Implementation of the neural network model in [2] |
Week 15 - 16:
12/04 - 12/17 | Deliverable #5 : CS297 report |
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:
- https://rajpurkar.github.io/SQuAD-explorer/
- Wang, Shuohang, and Jing Jiang. "Machine comprehension using match-lstm and answer pointer." arXiv preprint arXiv:1608.07905 (2016).
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