# Automatic question generation based on sentence structure analysis using machine learning approach

arXiv: https://arxiv.org/abs/2205.12811 · HTML: https://mlsanity.com/paper/2205.12811/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 72 references that are in the map.
- First posted on arXiv 25 May 2022.

## Builds on (most cited first, 11 of 11)

- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation](https://mlsanity.com/paper/1703.09902/) (2017)
- [Learning to Ask: Neural Question Generation for Reading Comprehension](https://mlsanity.com/paper/1705.00106/) (2017)
- [Neural Question Generation from Text: A Preliminary Study](https://mlsanity.com/paper/1704.01792/) (2017)
- [Question Answering and Question Generation as Dual Tasks](https://mlsanity.com/paper/1706.02027/) (2017)
- [Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus](https://mlsanity.com/paper/1603.06807/) (2016)
- [Machine Comprehension by Text-to-Text Neural Question Generation](https://mlsanity.com/paper/1705.02012/) (2017)
- [Evaluating Rewards for Question Generation Models](https://mlsanity.com/paper/1902.11049/) (2019)
- [Question Generation from a Knowledge Base with Web Exploration](https://mlsanity.com/paper/1610.03807/) (2016)
- [Putting the Horse Before the Cart:A Generator-Evaluator Framework for Question Generation from Text](https://mlsanity.com/paper/1808.04961/) (2018)
- [Training IBM Watson using Automatically Generated Question-Answer Pairs](https://mlsanity.com/paper/1611.03932/) (2016)

## Built on by (most cited first, 1 of 1)

- [idT5: Indonesian Version of Multilingual T5 Transformer](https://mlsanity.com/paper/2302.00856/) (2023)

## Abstract (arXiv)

Automatic question generation is one of the most challenging tasks of Natural Language Processing. It requires "bidirectional" language processing: firstly, the system has to understand the input text (Natural Language Understanding) and it then has to generate questions also in the form of text (Natural Language Generation). In this article, we introduce our framework for generating the factual questions from unstructured text in the English language. It uses a combination of traditional linguistic approaches based on sentence patterns with several machine learning methods. We firstly obtain lexical, syntactic and semantic information from an input text and we then construct a hierarchical set of patterns for each sentence. The set of features is extracted from the patterns and it is then used for automated learning of new transformation rules. Our learning process is totally data-driven because the transformation rules are obtained from a set of initial sentence-question pairs. The advantages of this approach lie in a simple expansion of new transformation rules which allows us to generate various types of questions and also in the continuous improvement of the system by reinforcement learning. The framework also includes a question evaluation module which estimates the quality of generated questions. It serves as a filter for selecting the best questions and eliminating incorrect ones or duplicates. We have performed several experiments to evaluate the correctness of generated questions and we have also compared our system with several state-of-the-art systems. Our results indicate that the quality of generated questions outperforms the state-of-the-art systems and our questions are also comparable to questions created by humans. We have also created and published an interface with all created datasets and evaluated questions, so it is possible to follow up on our work.