Fine-Tuned T5 Transformer with LSTM and Spider Monkey Optimizer for Redundancy Reduction in Automatic Question Generation
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s42979-024-02826-0.pdf
Reference40 articles.
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2. Perkoff E, Bhattacharyya A, Cai J, Cao J. Comparing neural question generation architectures for reading comprehension. 2023;556–566. https://doi.org/10.18653/v1/2023.bea-1.47.
3. Rathod M, Tu T, Stasaski K. Educational multi-question generation for reading comprehension. 2022;216–223. https://doi.org/10.18653/v1/2022.bea-1.26.
4. Thabet B, Zanichelli N, Zanichelli F. Q&A generation for flashcards within a transformer-based framework. 2023. https://doi.org/10.1007/978-3-031-29800-4_59.
5. Fuadi M, Wibawa A. Automatic question generation from Indonesian texts using text-to-text transformers. 2022;84–89. https://doi.org/10.1109/IEIT56384.2022.9967858.
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