Using NLP to generate Quizzes
Section: Full talk Technical level: Intermediate Session type: Lecture
- Quizzes/Questions are required by students for self-evaluation and by teachers to test students.
- However, quiz creation currently requires manual effort and understanding of the topic which making the process difficult to adapt and scale.
- Multiple-Choice Questions have been proved to evaluate student’s understanding of concepts and can exhibit broad ranges of difficulty and therefore, test different levels of understanding.
- However, MCQs require challenging options which can be cumbersome to pick.
- Using NLP to generate Multiple-Choice Questions from plain-text
- Statistically determines best entities to blank
- Generate semantically similar options based on context and corpus
- Better than subjective questions MC questions tests
- A user-interface to generate quizzes
- From any document
- On any topic in any genre (Educational or Recreational)
- From any paragraph
- Problem Statement
- How it’s done
- Brief introduction to NLP and NER (Named Entity Recognition)
- Using Named Entity Recognition to pick questions
- Ranking potential questions
- Picking relevant/similar options to accompany answer in MCQ
- Deploying the solution
- Scope and application of the solution
- Machine Learning Engineer at Freshworks since 2019. Work on building AI models for Freshsales, our Sales CRM.
- Worked with a number of startups on NLP and ML problems.
- Google Summer of Code‘18 under Debian