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Question Answering
Project Description
- Create a clone of SQuaD 2.0 in the Slovak language
- Setup annotation infrastructure with Prodigy
- Perform and evaluate annotations of Wikipedia data.
Auxiliary tasks:
- Consider using machine translation
- Train and evaluate Question Answering model
People
- Daniel Hládek (responsible researcher).
- Tomáš Kuchárik (student, help with web app).
- Ján Staš (BERT model).
- Ondrej Megela, Oleh Bilykh, Matej Čarňanský (auxiliary tasks).
- other students and annotators (annotations).
Tasks
Raw Data Preparation
Input: Wikipedia
Output: a set of paragraphs
- Obtaining and parsing of wikipedia dump
- Selecting feasible paragraphs
Done:
- Wiki parsing script (Daniel Hládek)
- PageRank script (Daniel Hládek)
- selection of paragraphs: select all good paragraphs and shuffle
- fix minor errors
To be done:
- Select the largest articles (to be compatible with squad).
Notes:
- PageRank Causes bias to geography, random selection might be the best
- 75 best articles
- 167 good articles
- Wiki Facts
Question Annotation
An annotation recipe for Prodigy
Input: A set of paragraphs
Output: 5 questions for each paragraph
Done:
- a data preparation script (Daniel Hládek)
- annotation recipe for Prodigy (Daniel Hládek)
- deployment at question.tukekemt.xyz (only from tuke) (Daniel Hládek)
- answer annotation together with question (Daniel Hládek)
- prepare final input paragraphs (dataset)
In progress:
- More annotations (volunteers and workers).
To be done:
- Prepare development set
Annotation Web Application
Annotation work summary, web applicatiobn
Input: Database of annotations
Output: Summary of work performed by each annotator
Done:
- application template (Tomáš Kuchárik)
- Dockerfile (Daniel Hládek)
- web application for annotation analysis in Flask (Tomáš Kuchárik, Daniel Hládek)
- application deployment (Daniel Hládek)
- extract annotations from question annotation in squad format (Daniel Hladek)
To be done:
- review of validations
Annotation Validation
Input: annnotated questions and paragraph
Output: good annotated questions
Done:
- Recipe for validations (binary annotation for paragraphs, question and answers, text fields for correction of question and answer). (Daniel Hládek)
- Deployment
To be done:
- Prepare for production
Annotation Manual
Output: Recommendations for annotators
Done:
- Web Page for annotators (Daniel Hládek)
- Modivation video (Daniel Hládek)
- Video with instructions (Daniel Hládek)
In progress:
- Should be instructions a part of the annotation webn application?
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)
In progress:
- Preliminary model (Ján Staš and Matej Čarňanský)
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 publications
Existing Datasets
- Squad The Stanford Question Answering Dataset(SQuAD) (Rajpurkar et al., 2016)
- WebQuestions
- Freebase
Intern tasks
Week 1: Intro
- Get acquainted with the project and Squad Database
- Download the database and study the bibliography
- Study Prodigy annnotation tool
- Read SQuAD: 100,000+ Questions for Machine Comprehension of Text
- Read Know What You Don't Know: Unanswerable Questions for SQuAD
Output:
- Short report
Week 2-4 The System
Select and train a working question answering system
Output:
- a deployment script with comments for a selected question answering system
Week 5-7 The Model
Take a working training recipe (can use English data), a script with comments or Jupyter Notebook
Output:
- a trained model
- evaluation of the model (if possible)