forked from KEMT/zpwiki
169 lines
3.9 KiB
Markdown
169 lines
3.9 KiB
Markdown
# 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
|
|
|
|
Input: A set of paragraphs
|
|
|
|
Output: A question for each paragraph
|
|
|
|
Done:
|
|
|
|
- a data preparation script
|
|
- annotation running script
|
|
|
|
To be done:
|
|
|
|
- final input paragraphs
|
|
- deployment
|
|
|
|
### Answer Annotation
|
|
|
|
Input: A set of paragraphs and questions
|
|
|
|
Output: An answer for each paragraph and question
|
|
|
|
Done:
|
|
|
|
- a data preparation script
|
|
- annotation running script
|
|
|
|
To be done:
|
|
|
|
- input paragraphs with questions
|
|
- deployment
|
|
|
|
### Annotation Summary
|
|
|
|
Annotation work summary
|
|
|
|
Input: Database of annotations
|
|
|
|
Output: Summary of work performed by each annotator
|
|
|
|
To be done:
|
|
|
|
- web application for annotation analysis
|
|
- analyze sql schema and find out who annotated what
|
|
|
|
### Annotation Manual
|
|
|
|
Output: Recommendations for annotators
|
|
|
|
TBD
|
|
|
|
### 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
|
|
|
|
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)
|
|
|
|
|
|
|