A Systematic Literature Review of Question Answering: Research Trends, Datasets, Methods

Bakır, Dilan; Aktaş, Mehmet S.

doi:10.1007/978-3-031-10536-4_4

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Methods 21 Data Set Collection1

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2022

2024

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Cited by 3 publications

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“…To train a neural model we need to get question and answer inputs. There are a large number of publicly available question and answer datasets [60]. The AI2 reasoning challenge (ARC) dataset includes 7,787 multiple-choice science questions that created for grade-school level students [61].…”

Section: Methods 21 Data Set Collectionmentioning

confidence: 99%

Development of system for generating questions, answers, distractors using transformers

Barlybayev,

Matkarimov

2024

IJECE

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The goal of this article is to develop a multiple-choice questions generation system that has a number of advantages, including quick scoring, consistent grading, and a short exam period. To overcome this difficulty, we suggest treating the problem of question creation as a sequence-to-sequence learning problem, where a sentence from a text passage can directly mapped to a question. Our approach is data-driven, which eliminates the need for manual rule implementation. This strategy is more effective and gets rid of potential errors that could result from incorrect human input. Our work on question generation, particularly the usage of the transformer model, has been impacted by recent developments in a number of domains, including neural machine translation, generalization, and picture captioning.

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Section: Methods 21 Data Set Collectionmentioning

confidence: 99%