# Question Answering

[Project repository](https://git.kemt.fei.tuke.sk/dano/annotation) (private)

## Project Description

- 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/)
- Perform and evaluate annotations of [Wikipedia data](https://dumps.wikimedia.org/backup-index.html).

Auxiliary tasks:

- Consider using machine translation 
- Train and evaluate Question Answering model

## Tasks

### Raw Data Preparation

Input: Wikipedia

Output: a set of paragraphs

1. Obtaining and parsing of wikipedia dump
1. Selecting feasible paragraphs

Done:

- Wiki parsing script
- PageRank script

To be done:

- random selection of paragraphs: select all good paragraphs and shuffle

Notes:

- PageRank Causes bias to geography, random selection might be the best
- [75 best articles](https://sk.wikipedia.org/wiki/Wikip%C3%A9dia:Zoznam_najlep%C5%A1%C3%ADch_%C4%8Dl%C3%A1nkov)
- [167 good articles](https://sk.wikipedia.org/wiki/Wikip%C3%A9dia:Zoznam_dobr%C3%BDch_%C4%8Dl%C3%A1nkov)
- [Wiki Facts](https://sk.wikipedia.org/wiki/Wikip%C3%A9dia:Zauj%C3%ADmavosti)

### Question Annotation

An annotation recipe for Prodigy

Input: A set of paragraphs

Output: A question for each paragraph

Done: 

- a data preparation script
- annotation recipe
- deployment at [question.tukekemt.xyz](http://question.tukekemt.xyz) (only from tuke)
- answer annotation together with question
 
To be done:

- prepare final input paragraphs (dataset)


### Answer Annotation

Input: A set of paragraphs and questions

Output: An answer for each paragraph and question

Done: 

- a data preparation script
- annotation recipe
- deployment at [answer.tukekemt.xyz](http://answer.tukekemt.xyz) (only from tuke)

To be done:

- extract annotations from question annotation
- input paragraphs with questions (dataset)

### Annotation Summary

Annotation work summary

Input: Database of annotations

Output: Summary of work performed by each annotator

Done:

- application template
- Dockerfile

In progress:

- web application for annotation analysis (Tomáš Kuchárik, Flask)
- application deployment

To be done:
- analyze sql schema and find out who annotated what

### Annotation Manual

Output: Recommendations for annotators

To be done:

- Web Page for annotators

### Question Answering Model

Training the model with annotated data

Input: An annotated QA database

Output: An evaluated model for QA

To be done:

- Selecting existing modelling approach
- Evaluation set selection
- Model evaluation
- Supporting the annotation with the model (pre-selecting answers)

### Supporting activities

Output: More annotations

Organizing voluntary student challenges to support the annotation process

TBD

## Existing implementations

- https://github.com/facebookresearch/DrQA
- https://github.com/brmson/yodaqa
- https://github.com/5hirish/adam_qas
- https://github.com/WDAqua/Qanary - metodológia a implementácia QA

## Bibligraphy

- Reading Wikipedia to Answer Open-Domain Questions, Danqi Chen, Adam Fisch, Jason Weston, Antoine Bordes
Facebook Research
- SQuAD: 100,000+ Questions for Machine Comprehension of Text https://arxiv.org/abs/1606.05250
- [WDaqua](https://wdaqua.eu/our-work/) publications

## Existing Datasets

- [Squad](https://rajpurkar.github.io/SQuAD-explorer/)  The Stanford Question Answering Dataset(SQuAD)  (Rajpurkar  et  al.,  2016) 
- [WebQuestions](https://github.com/brmson/dataset-factoid-webquestions)
- [Freebase](https://en.wikipedia.org/wiki/Freebase)

## Intern tasks

Week 1: Intro

- Get acquainted with the project and Squad Database
- Download the database and study the bibliography
- Study [Prodigy annnotation](https://Prodi.gy) tool
- Read [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://arxiv.org/abs/1606.05250)
- Read [Know What You Don't Know: Unanswerable Questions for SQuAD](https://arxiv.org/abs/1806.03822)

Week 2 and 3: Web Application

- Analyze sql schema of Prodigy annotations 
- Find out who annotated what.
- Make a web application that displays results.
- Extend the application to analyze more Prodigy instances (for both question and answer annotations)
- Improve the process of annotation.

Output: Web application (in Node.js or Python) and Dockerfile

Week 4-7 The model

Select and train a working question answering system

Output:

- a deployment script with comments for a selected question answering system 
- a working training recipe (can use English data), a script with comments or Jupyter Notebook
- a trained model
- evaluation of the model (if possible)