forked from KEMT/zpwiki
175 lines
4.3 KiB
Markdown
175 lines
4.3 KiB
Markdown
# Question Answering
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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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- 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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Auxiliary tasks:
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- Consider using machine translation
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- Train and evaluate Question Answering model
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## Tasks
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### Raw Data Preparation
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Input: Wikipedia
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Output: a set of paragraphs
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1. Obtaining and parsing of wikipedia dump
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1. Selecting feasible paragraphs
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Done:
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- Wiki parsing script
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- PageRank script
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To be done:
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- random selection of paragraphs: select all good paragraphs and shuffle
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Notes:
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- PageRank Causes bias to geography, random selection might be the best
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- [75 best articles](https://sk.wikipedia.org/wiki/Wikip%C3%A9dia:Zoznam_najlep%C5%A1%C3%ADch_%C4%8Dl%C3%A1nkov)
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- [167 good articles](https://sk.wikipedia.org/wiki/Wikip%C3%A9dia:Zoznam_dobr%C3%BDch_%C4%8Dl%C3%A1nkov)
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- [Wiki Facts](https://sk.wikipedia.org/wiki/Wikip%C3%A9dia:Zauj%C3%ADmavosti)
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### Question Annotation
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An annotation recipe for Prodigy
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Input: A set of paragraphs
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Output: A question for each paragraph
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Done:
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- a data preparation script
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- annotation annotation recipe
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- deployment at [question.tukekemt.xyz](http://question.tukekemt.xyz) (only from tuke)
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- answer annotation together with question
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To be done:
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- prepare final input paragraphs (dataset)
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### Answer Annotation
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Input: A set of paragraphs and questions
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Output: An answer for each paragraph and question
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Done:
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- a data preparation script
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- annotation recipe
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- deployment at [answer.tukekemt.xyz](http://answer.tukekemt.xyz) (only from tuke)
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To be done:
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- extraxt annotations from question annotation
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- input paragraphs with questions (dataset)
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### Annotation Summary
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Annotation work summary
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Input: Database of annotations
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Output: Summary of work performed by each annotator
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In progress:
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- web application for annotation analysis (Tomáš Kuchárik, Flask)
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To be done:
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- analyze sql schema and find out who annotated what
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### Annotation Manual
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Output: Recommendations for annotators
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To be done:
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- Web Page for annotators
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### Question Answering Model
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Training the model with annotated data
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Input: An annotated QA database
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Output: An evaluated model for QA
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To be done:
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- Selecting existing modelling approach
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- Evaluation set selection
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- Model evaluation
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- Supporting the annotation with the model (pre-selecting answers)
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### Supporting activities
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Output: More annotations
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Organizing voluntary student challenges to support the annotation process
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TBD
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## Existing implementations
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- https://github.com/facebookresearch/DrQA
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- https://github.com/brmson/yodaqa
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- https://github.com/5hirish/adam_qas
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- https://github.com/WDAqua/Qanary - metodológia a implementácia QA
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## Bibligraphy
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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](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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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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- Analyze sql schema of Prodigy annotations
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- Find out who annotated what.
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- Make a web application that displays results.
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- Extend the application to analyze more Prodigy instances (for both question and answer annotations)
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- Improve the process of annotation.
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Output: Web application (in Node.js or Python) and Dockerfile
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Week 4-7 The model
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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 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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