forked from KEMT/zpwiki
213 lines
5.3 KiB
Markdown
213 lines
5.3 KiB
Markdown
---
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title: Question Answering
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published: true
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taxonomy:
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category: [project]
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tag: [annotation,question-answer,nlp]
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author: Daniel Hladek
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---
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# Question Answering
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- [Project repository](https://git.kemt.fei.tuke.sk/dano/annotation) (private)
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- [Annotation Manual for question annotation](navod)
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- [Annotation Manual for validations](validacie)
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- [Summary database application](https://app.question.tukekemt,xyz)
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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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## People
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- Daniel Hládek (responsible researcher).
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- Tomáš Kuchárik (student, help with web app).
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- Ján Staš (BERT model).
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- [Ondrej Megela](/students/2018/ondrej_megela), [Oleh Bilykh](/students/2018/oleh_bilykh), Matej Čarňanský (auxiliary tasks).
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- other students and annotators (annotations).
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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 (Daniel Hládek)
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- PageRank script (Daniel Hládek)
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- selection of paragraphs: select all good paragraphs and shuffle
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- fix minor errors
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To be done:
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- Select the largest articles (to be compatible with squad).
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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: 5 questions for each paragraph
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Done:
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- a data preparation script (Daniel Hládek)
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- annotation recipe for Prodigy (Daniel Hládek)
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- deployment at [question.tukekemt.xyz](http://question.tukekemt.xyz) (only from tuke) (Daniel Hládek)
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- answer annotation together with question (Daniel Hládek)
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- prepare final input paragraphs (dataset)
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In progress:
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- More annotations (volunteers and workers).
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To be done:
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- Prepare development set
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### Annotation Web Application
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Annotation work summary, web applicatiobn
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Input: Database of annotations
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Output: Summary of work performed by each annotator
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Done:
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- application template (Tomáš Kuchárik)
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- Dockerfile (Daniel Hládek)
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- web application for annotation analysis in Flask (Tomáš Kuchárik, Daniel Hládek)
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- application deployment (Daniel Hládek)
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- extract annotations from question annotation in squad format (Daniel Hladek)
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To be done:
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- review of validations
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### Annotation Validation
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Input: annnotated questions and paragraph
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Output: good annotated questions
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Done:
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- Recipe for validations (binary annotation for paragraphs, question and answers, text fields for correction of question and answer). (Daniel Hládek)
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- Deployment
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To be done:
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- Prepare for production
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### Annotation Manual
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Output: Recommendations for annotators
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Done:
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- Web Page for annotators (Daniel Hládek)
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- Modivation video (Daniel Hládek)
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- Video with instructions (Daniel Hládek)
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In progress:
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- Should be instructions a part of the annotation webn application?
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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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In progress:
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- Preliminary model (Ján Staš and Matej Čarňanský)
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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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- Read [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://arxiv.org/abs/1606.05250)
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- Read [Know What You Don't Know: Unanswerable Questions for SQuAD](https://arxiv.org/abs/1806.03822)
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Output:
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- Short report
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Week 2-4 The System
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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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Week 5-7 The Model
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Take a working training recipe (can use English data), a script with comments or Jupyter Notebook
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Output:
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- a trained model
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- evaluation of the model (if possible)
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