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# Question Answering # Question Answering
[Project repository](https://git.kemt.fei.tuke.sk/dano/annotation) [Project repository](https://git.kemt.fei.tuke.sk/dano/annotation) (private)
## Project Description ## Project Description
Task definition:
- Create a clone of [SQuaD 2.0](https://rajpurkar.github.io/SQuAD-explorer/) in the Slovak language - Create a clone of [SQuaD 2.0](https://rajpurkar.github.io/SQuAD-explorer/) in the Slovak language
- Setup annotation infrastructure with [Prodigy](https://prodi.gy/) - Setup annotation infrastructure with [Prodigy](https://prodi.gy/)
- Perform and evaluate annotations of [Wikipedia data](https://dumps.wikimedia.org/backup-index.html). - Perform and evaluate annotations of [Wikipedia data](https://dumps.wikimedia.org/backup-index.html).
@ -129,12 +127,13 @@ TBD
- Reading Wikipedia to Answer Open-Domain Questions, Danqi Chen, Adam Fisch, Jason Weston, Antoine Bordes - Reading Wikipedia to Answer Open-Domain Questions, Danqi Chen, Adam Fisch, Jason Weston, Antoine Bordes
Facebook Research Facebook Research
- SQuAD: 100,000+ Questions for Machine Comprehension of Text https://arxiv.org/abs/1606.05250 - SQuAD: 100,000+ Questions for Machine Comprehension of Text https://arxiv.org/abs/1606.05250
- [WDaqua](https://wdaqua.eu/our-work/) publications
## Existing Datasets ## Existing Datasets
- Squad TheStanfordQuestionAnsweringDataset(SQuAD) (Rajpurkar et al., 2016) - [Squad](https://rajpurkar.github.io/SQuAD-explorer/) The Stanford Question Answering Dataset(SQuAD) (Rajpurkar et al., 2016)
- WebQuestions - [WebQuestions](https://github.com/brmson/dataset-factoid-webquestions)
- https://en.wikipedia.org/wiki/Freebase - [Freebase](https://en.wikipedia.org/wiki/Freebase)
## Intern tasks ## Intern tasks
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- Get acquainted with the project and Squad Database - Get acquainted with the project and Squad Database
- Download the database and study the bibliography - Download the database and study the bibliography
- Study [Prodigy annnotation](https://Prodi.gy) tool
Week 2 and 3: Web Application Week 2 and 3: Web Application
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Output: Output:
- a deployment script with comments for a selected question answering system - a deployment script with comments for a selected question answering system
- a working training recipe (can use English ata), a script with comments or Jupyter Notebook - a working training recipe (can use English data), a script with comments or Jupyter Notebook
- a trained model - a trained model
- evaluation of the model (if possible) - evaluation of the model (if possible)