QBERT: Generalist Model for Processing Questions

Xu, Zhaozhen; Cristianini, Nello

doi:10.1007/978-3-031-30047-9_37

Lecture Notes in Computer Science

2023

DOI: 10.1007/978-3-031-30047-9_37

|View full text |Cite

QBERT: Generalist Model for Processing Questions

Zhaozhen Xu¹,

Nello Cristianini²

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

2024

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Understanding Visitors’ Curiosity in a Science Centre with Deep Question Processing Network

Xu,

Howarth,

Briggs

et al. 2023

Int J Artif Intell Educ

View full text Add to dashboard Cite

Questions have a critical role in learning and teaching. People ask questions to obtain information and express interest in ideas. The Bristol scientific centre “We The Curious” launched “Project What If” in 2017 to inspire residents of Bristol to record their questions and pursue their curiosities. Researching these questions may help the museum better understand the curiosity of its audiences and create exhibitions or educational content that are more relevant to their interests and lives. The project managed to collect more than 10,000 questions on various topics, and more questions are being collected on a daily basis. With this large amount of data collected, it is time-consuming to process and analyse all the questions by humans. This research aims to apply artificial intelligence (AI) techniques and models in analysing these questions gathered by We The Curious. Meanwhile, in AI, there is a lack of tools that focus on processing and analysing the questions. Thus, we introduce a deep neural network called QBERT to process the questions for three tasks: question taxonomy, equivalent question detection, and question answering. Then we apply QBERT to provide an analysis of the questions collected by We The Curious, as well as comprehend Bristolians’ curiosity. Then using QBERT, we categorise the We The Curious questions into 90 themes and 5,930 communities. Moreover, 436 questions are answered by one-sentence answers extracted from Wikipedia.

show abstract

Understanding Visitors’ Curiosity in a Science Centre with Deep Question Processing Network

Xu,

Howarth,

Briggs

et al. 2023

Int J Artif Intell Educ

View full text Add to dashboard Cite

show abstract

Advances in the Neural Network Quantization: A Comprehensive Review

Wei,

Ma,

Yang

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

Artificial intelligence technologies based on deep convolutional neural networks and large language models have made significant breakthroughs in many tasks, such as image recognition, target detection, semantic segmentation, and natural language processing, but also face a conflict between the high computational capacity of the algorithms and limited deployment resources. Quantization, which converts floating-point neural networks into low-bit-width integer networks, is an important and essential technique for efficient deployment and cost reduction in edge computing. This paper analyzes various existing quantization methods, showcases the deployment accuracy of advanced techniques, and discusses the future challenges and trends in this domain.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

QBERT: Generalist Model for Processing Questions

Cited by 2 publications

References 11 publications

Understanding Visitors’ Curiosity in a Science Centre with Deep Question Processing Network

Understanding Visitors’ Curiosity in a Science Centre with Deep Question Processing Network

Advances in the Neural Network Quantization: A Comprehensive Review

Contact Info

Product

Resources

About