The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Study on Sentence and Question Formation Using Deep Learning Techniques
|
|
Author(s): N. Venkateswaran (Department of Management Studies, Panimalar Engineering College, India), R. Vidhya (Department of Computer Science and Engineering, Sri Krishna College of Technology, India), Darshana A. Naik (Ramaiah Institute of Technology, India), T. F. Michael Raj (Department of Computer Applications, SCLAS, SIMATS University (Deemed), India), Neha Munjal (Department of Physics, Lovely Professional University, India)and Sampath Boopathi (Mechanical Engineering, Muthayammal Engineering College, India)
Copyright: 2023
Pages: 22
Source title:
Digital Natives as a Disruptive Force in Asian Businesses and Societies
Source Author(s)/Editor(s): Omkar Dastane (UCSI Graduate Business School, UCSI University, Malaysia), Aini Aman (Universiti Kebangsaan Malaysia, Malaysia)and Nurhizam Safie Bin Mohd Satar (Universiti Kebangsaan Malaysia, Malaysia)
DOI: 10.4018/978-1-6684-6782-4.ch015
Purchase
|
Abstract
Natural language techniques require less personal information to communicate between computers and people. Generative models can create text for machine translation, summarization, and captioning without the need for dataset labelling. Markov chains and hidden Markov models can also be employed. A language model that can produce sentences word by word was created using RNNs (recurrent neural networks), LSTMs (long short-term memory model), and GRUs (gated recurrent unit). The suggested method compares RNN, LSTM, and GRU networks to see which produces the most realistic text and how training loss varies with iterations. Cloze questions feature alternative responses with distractors, whereas open-cloze questions include instructive phrases with one or more gaps. This chapter provides two novel ways to generate distractors for computer-aided exams that are simple and dependable.
Related Content
|
Wilson Yule.
© 2024.
14 pages.
|
|
Sonali Sugrim.
© 2024.
17 pages.
|
|
Pitshou Moleka Basikabio.
© 2024.
16 pages.
|
|
Subhajit Panda, Navkiran Kaur.
© 2024.
35 pages.
|
|
Debalina Mukherjee, Anita Basak.
© 2024.
14 pages.
|
|
Selema Tebogo Molopa, Ontebetse Mothopeng, Mbali Zulu, Ednah Mokale Kgasi, Nyameka Mila-Ndenge, Nokuthula Ndlovu, Cavall Jocelyn Moira Barends, Xabiso Xesi, Yanga Levi.
© 2024.
28 pages.
|
|
Sihle Blose, Mahlaga J. Molepo.
© 2024.
17 pages.
|
|
|