A Comprehensive Survey on Neural Question Generation: Methods, Applications, and Prospects

المفاهيم الأساسية

The author explores the advancements in Neural Question Generation (NQG) by categorizing approaches and discussing their strengths and limitations.

الملخص

This survey delves into the background, methods, applications, and future trends of NQG. It covers structured NQG using knowledge bases, unstructured NQG from texts and images, and hybrid approaches. The paper highlights the shift from rule-based to neural network-based models like PLMs for improved performance in question generation tasks.

الإحصائيات

"22,989 instances from WebQuestionsSP and ComplexWebQuestions" - Kumar et al., 2019a.
"3,563,535 questions in MS MARCO dataset" - Nguyen et al., 2016.
"369,861 questions in VQA dataset" - Antol et al., 2015.
"127,000 QA pairs in CoQA dataset" - Reddy et al., 2019.
"25,000 questions in VQG Commonsense dataset" - Mostafazadeh et al., 2016.

اقتباسات

"In this survey, we present a detailed examination of the advancements in Neural Question Generation (NQG)." - Authors
"The field has progressively transitioned from rule-based approaches to neural network-based methods." - Authors
"With the continuous scaling of PLMs in terms of parameter size and training corpus volume..." - Authors

الرؤى الأساسية المستخلصة من

A Survey on Neural Question Generation

by Shasha Guo,L... في arxiv.org 02-29-2024

https://arxiv.org/pdf/2402.18267.pdf

استفسارات أعمق

How can proactive question generation enhance user experiences beyond reactive question generation?

Proactive question generation goes beyond simply reacting to inputs by anticipating user needs and preferences. By tailoring questions to meet specific user requirements and predefined targets, proactive question generation can significantly enhance user experiences in various ways:

Personalization: Proactively generating questions allows for a more personalized experience tailored to individual users' learning styles, preferences, and knowledge levels. This customization can lead to higher engagement and better retention of information.

Progressive Learning: In educational applications, proactive question generation can guide students through a progressive learning path from simple concepts to more advanced topics. By providing targeted questions that build upon previous knowledge, students can grasp complex ideas more effectively.

Efficiency: Anticipating the next set of questions based on user interactions streamlines the learning process by presenting relevant content at the right time. This efficiency saves users time and effort in navigating through vast amounts of information.

Engagement: Proactively generated questions create an interactive environment that keeps users engaged and motivated throughout their learning journey or interaction with a system.

Adaptability: By adjusting the difficulty level or type of questions based on real-time feedback or performance metrics, proactive question generation ensures adaptive learning experiences that cater to individual needs.

In essence, proactive question generation transforms passive interactions into dynamic engagements that actively support users in achieving their goals.

What are the implications of multi-modal question generation for educational applications?

Multi-modal question generation holds significant implications for educational applications due to its ability to leverage diverse data sources such as text, images, and other modalities simultaneously:

Enhanced Learning Experience: Incorporating multiple modalities into question generation provides a richer context for learners by engaging different senses (visual, auditory) and cognitive processes (reading comprehension vs visual interpretation). This holistic approach enhances understanding and retention of information.

Catering to Different Learning Styles: Students have varied learning styles – some may prefer visual aids while others excel with textual explanations.
Multi-modal questioning accommodates these differences by offering questions in formats that align with diverse learning preferences.

Improved Critical Thinking Skills: Multi-modal questions challenge students' critical thinking abilities by requiring them to analyze information across different modalities before formulating responses.

4Interactive Assessments: Educational assessments become more interactive when incorporating multi-modal elements into questioning techniques.
Such assessments not only test knowledge but also encourage creativity,
problem-solving skills,
analytical thinking,
etc., enhancing overall cognitive development
5Real-world Relevance: Many real-world scenarios require individuals
to interpret information from various sources simultaneously.
By practicing multi-modal questioning in education,
students develop skills applicable outside academic settings

How can automatic evaluation metrics be improved

to capture diverse aspects of
question quality beyond lexical overlap?
Automatic evaluation metrics play a crucial role in assessing
the quality of generated questions; however,
they often fall short in capturing diverse aspects
of quality beyond lexical overlap.
To improve these metrics
and provide a comprehensive evaluation
of question quality,
1Semantic Coherence Metrics:
Introduce metrics that evaluate how well-generated
questions maintain semantic coherence within the given context.
This could involve measuring logical consistency,
relevance to surrounding text/image content,
and adherence to contextual constraints
2Contextual Understanding Metrics:
Develop metrics that assess how well-generated
questions demonstrate an understanding
of the broader context provided—whether it's textual passages or visual scenes.
Metrics should consider if generated queries address key points within the context accurately
3Question Specificity Metrics:
Create measures focusing on how specific
generated inquiries are rather than generic ones;
this includes evaluating whether they target precise details or concepts within texts/images instead
4**Diversity Metrics Beyond N-grams:
Expand diversity metrics beyond n-grams-based approaches;
consider evaluating uniqueness at sentence-level structures,
thematic variety across sets of queries,and syntactic diversity among generated items
5**Answerability Assessment:
Incorporate answerability assessment into evaluation criteria;
determine if generated queries are clear enough for accurate answers—evaluating factors like ambiguity levels,redundancy,and completeness
6*Human-Likeness Evaluation:
Include human-likeness evaluations where subjective judgments are made regarding naturalness,pertinence,and fluency—the goal is ensuring machine-generated queries resemble those crafted by humans
By integrating these enhanced evaluation dimensions,into existing metric frameworks,the assessment accuracy will increase,resulting,in amore nuanced understanding o fquestionqualitybeyondlexicaloverlap