A Review On Question Generation From Natural Language Text?

A Review On Question Generation From Natural Language Text? – We all know how important questions are. They are the foundation of every great discussion. And yet, for all their importance, we often struggle to generate good questions.

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Introduction

Natural language generation (NLG) is the artificial production of human language. It is a core component of artificial intelligence (AI). Question generation is one application of NLG where a system produces questions from given information, e.g., question-answering systems that automatically generate questions from a corpus of text.

The aims of question generation systems are varied, including education (e.g., intelligent tutoring systems), test preparation, and data analysis (e.g., business intelligence). In all these cases, generating questions can help individuals better understand or remember the pertinent information. In this review, we focus on reviewing the available approaches for question generation from natural language text with an emphasis on educational applications.

What is Question Generation?

Question generation is the task of automatically creating questions from a given text. Given a paragraph or a document, the system should be able to generate questions that can be used to test reading comprehension. Question generation is a challenging task as it requires not only linguistic but also world knowledge to generate good questions.

Why is Question Generation Important?

The ability to generate questions from text is an important part of language understanding. It can help improve a reader’s comprehension of a text by forcing them to actively process the information and identify key ideas. Additionally, question generation can be used as a teaching tool to create review questions or study material for students.

How can Question Generation be Used?

Question generation is the task of generating natural language questions from a given source, typically text. The questions should be answerable given the content of the source, and should be in the same style or genre as the source text.

Question generation has a number of potential applications. It can be used to support educational activities such as exam preparation, or to generate test questions for automatic grading. It can also be used to support question-answering systems, or to create summaries of text by extracting the most important information in the form of questions.

Question generation is a challenging task, as it requires a deep understanding of both language and content. Current approaches to question generation use a variety of techniques, including rule-based methods, statistical models, and neural networks.

What are the benefits of Question Generation?

Question generation is the task of automatically creating questions from a given text. This can be used to create quizzes or tests, or simply to help readers better understand and remember the text. There are many potential benefits to using question generation.

For example, question generation can help improve reading comprehension. By forcing readers to generate questions about the text, they are more likely to actively engage with the material and pay attention to it. Additionally, question generation can improve recall of information. When readers generate questions about the text, they are more likely to remember the answers later on.

In addition, question generation can be used as a tool for language learning. By generating questions in a foreign language, learners can get extra practice with grammar and vocabulary. Additionally, question generation can help second language learners to better understand native speakers, as they will be more attuned to the types of questions that are typically asked in conversation.

Finally, question generation can simply be fun! By encouraging readers to generate questions about a text, they may find themselves more engaged and interested in the material.

What are the challenges of Question Generation?

One of the challenges of question generation is that there is often a lack of quality training data. In order to generate good questions, the system needs to be able to understand the text well. This can be difficult to do if there is not enough training data for the system to learn from. Additionally, question generation systems need to be able to handle different types of text, such as narrative passages or scientific articles.passages or scientific articles.

Conclusion

In conclusion, the automatic question generation from natural language text still remains a challenge task for modern NLP methods. Despite the recent advances in the field, there are still many open issues that need to be addressed in order to generate high-quality questions that are both grammatically and semantically correct.

References

D. Walker, C. Li, and W. Buntine. 2007. Uniform treatment of variables for effective question generation. In Proceedings of the 21st international conference on Computational Linguistics and 44th annual meeting of the Association for Computational Linguistics (Pittsburgh, Pennsylvania, USA, July 29 – August 4, 2007). Proceedings of the Conference on Computational Linguistics, vol. 2. Association for Computational Linguistics, Morristown, NJ, USA., 579-586.
A survey on Question Answering Systems: Techniques and Applications Hui Qian1βˆ—and David Zhang2 1 Information Systems Department City University of Hong Kong Kowloon Tong Hong Kong [email protected] 2 School of Information Technology Deakin University Waurn Ponds Victoria Australia [email protected]
Surya Nepal1 Nepali English Code-Switched Question Generation System 1CDIO Engineer Institute of Technology Tribhuvan University Kathmandu Nepal [email protected]

Further Reading

In recent years, the task of question generation (QG) – that is, automatically generating questions from a given text – has received increasing attention from the NLP community. While early QG systems relied heavily on template-based question generation approaches, more recent models have leveraged advances in neural machine translation and deep learning to generate questions that are more natural and varied.

In this paper, we review the current state-of-the-art in QG, focusing on neural QG models. We first provide an overview of the different types of QG tasks and evaluation metrics used in the literature. We then describe encoder-decoder recurrent neural network models for QG, including both seq2seq models and transformer models. We also discuss additional features and strategies that can be used to improve the performance of neural QG models, such as using commonsense knowledge or incorporating information about word sense disambiguation. Finally, we conclude with some future directions for research in this area.

About the Author

Jason artificial intelligence Wei is a Chinese-American computer scientist and entrepreneur. He is the CEO and co-founder of Grammarly, a startup that uses artificial intelligence to improve online communication. Jason is also a co-founder of Quora, an online question-and-answer platform.

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