Generating questions from text (NLP)

As of 2022, Haystack provides a comprehensive suite of tools to accomplish the purpose of Question generation and answering using the latest and greatest Transformer models and Transfer learning.

From their website,

Haystack is an open-source framework for building search systems that work intelligently over large document collections. Recent advances in NLP have enabled the application of question answering, retrieval and summarization to real world settings and Haystack is designed to be the bridge between research and industry.

• NLP for Search: Pick components that perform retrieval, question answering, reranking and much more
• Latest models: Utilize all transformer based models (BERT, RoBERTa, MiniLM, DPR) and smoothly switch when new ones get published
• Flexible databases: Load data into and query from a range of databases such as Elasticsearch, Milvus, FAISS, SQL and more
• Scalability: Scale your system to handle millions of documents and deploy them via REST API
• Domain adaptation: All tooling you need to annotate examples, collect user-feedback, evaluate components and finetune models.

Based on my personal experience, I am 95% successful in generating Questions and Answers in my Internship for training purposes. I have a sample web user interface to demonstrate and the code too. My Web App and Code.

Huge shoutout to the developers on the Slack channel for helping noobs in AI like me! Implementing and deploying a NLP model has never been easier if not for Haystack. I believe this is the only tool out there where one can easily develop and deploy.

Disclaimer: I do not work for deepset.ai or Haystack, am just a fan of haystack.

In addition to the link to Michael Heilman's PhD provided by dmn, I recommend checking out the following papers:

• Automatic Question Generation and Answer Judging: A Q&A Game for Language Learning (Yushi Xu, Anna Goldie, Stephanie Seneff)
• Automatic Question Generationg from Sentences (Husam Ali, Yllias Chali, Sadid A. Hasan)

Unfortunately there isn't one, exactly. There is some code written as part of Michael Heilman's PhD dissertation at CMU; perhaps you'll find it and its corresponding papers interesting?

If it helps, the topic you want information on is called "question generation". This is pretty much the opposite of what Watson does, even though "here is an answer, generate the corresponding question" is exactly how Jeopardy is played. But actually, Watson is a "question answering" system.