Improving question answering performance using knowledge distillation and active learning

Boreshban, Yasaman; Mirbostani, Seyed Morteza; Ghassem-Sani, Gholamreza; Mirroshandel, Seyed Abolghasem; Amiriparian, Shahin

doi:10.1016/j.engappai.2023.106137

Cited by 6 publications

(1 citation statement)

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“…It has been demonstrated to be effective for activities like object and voice recognition. Numerous previously trained models were utilized as the initial teaching emulate in one research, and their experience was then transmitted to a number of subordinate QA models, improving the QA performance [129]. A further investigation employed supervised transfer learning to improve the accuracy of the item details, and it saw an improvement of roughly 10% [130].…”

Section: Critical Analysismentioning

confidence: 99%

Deep Learning Based Question Answering System (Survey)

Saeed,

ashraf,

Jhanjhi

2023

Preprint

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Deep learning-based question answering systems have transformed the discipline of natural language processing (NLP) by automating the extraction of answers from textual data. This survey paper provides a captivating overview of these systems, exploring methodologies, techniques, and architectures such as recurrent neural networks (RNNs), BERT model, and transformer models. Extractive and generative approaches are examined, alongside the challenges of handling complex questions, managing noisy input, and addressing rare or unseen words. This survey serves as a stimulating reference, offering valuable insights to researchers and practitioners, fueling innovation and advancement in question answering systems within NLP.

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Section: Critical Analysismentioning

confidence: 99%