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# Question Answering
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[Project repository](https://git.kemt.fei.tuke.sk/dano/annotation)
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[Project repository](https://git.kemt.fei.tuke.sk/dano/annotation) (private)
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## Project Description
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Task definition:
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- Create a clone of [SQuaD 2.0](https://rajpurkar.github.io/SQuAD-explorer/) in the Slovak language
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- Setup annotation infrastructure with [Prodigy](https://prodi.gy/)
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- Perform and evaluate annotations of [Wikipedia data](https://dumps.wikimedia.org/backup-index.html).
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@ -129,12 +127,13 @@ TBD
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- Reading Wikipedia to Answer Open-Domain Questions, Danqi Chen, Adam Fisch, Jason Weston, Antoine Bordes
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Facebook Research
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- SQuAD: 100,000+ Questions for Machine Comprehension of Text https://arxiv.org/abs/1606.05250
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- [WDaqua](https://wdaqua.eu/our-work/) publications
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## Existing Datasets
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- Squad TheStanfordQuestionAnsweringDataset(SQuAD) (Rajpurkar et al., 2016)
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- WebQuestions
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- https://en.wikipedia.org/wiki/Freebase
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- [Squad](https://rajpurkar.github.io/SQuAD-explorer/) The Stanford Question Answering Dataset(SQuAD) (Rajpurkar et al., 2016)
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- [WebQuestions](https://github.com/brmson/dataset-factoid-webquestions)
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- [Freebase](https://en.wikipedia.org/wiki/Freebase)
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## Intern tasks
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@ -142,6 +141,7 @@ Week 1: Intro
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- Get acquainted with the project and Squad Database
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- Download the database and study the bibliography
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- Study [Prodigy annnotation](https://Prodi.gy) tool
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Week 2 and 3: Web Application
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@ -160,7 +160,7 @@ Select and train a working question answering system
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Output:
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- a deployment script with comments for a selected question answering system
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- a working training recipe (can use English ata), a script with comments or Jupyter Notebook
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- a working training recipe (can use English data), a script with comments or Jupyter Notebook
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- a trained model
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- evaluation of the model (if possible)
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