Automatic multiple-choice question generation from Thai text

Kwankajornkiet, Chonlathorn; Suchato, Atiwong; Punyabukkana, Proadpran

doi:10.1109/jcsse.2016.7748891

Cited by 11 publications

(7 citation statements)

References 5 publications

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“…Among these are selection of distractors based on word frequency (i.e. the number of times distractors appear in a corpus is similar to the key) ( Kwankajornkiet et al 2016;Susanti et al 2015) selected distractors that are declared in a KB to be siblings of the key, which also implies some notion of similarity (siblings are assumed to be similar). Another approach that relies on structured knowledge sources is described in Seyler et al (2017).…”

Section: Generation Tasksmentioning

confidence: 99%

“…The ranking goal is to prioritise good quality questions. The ranking is achieved by the use of statistical models as in Blšták (2018), Kwankajornkiet et al (2016), Liu et al (2017), and Niraula and Rus (2015).…”

Section: Generation Tasksmentioning

confidence: 99%

“…Free-response questions are more prevalent among studies using text, with 21 studies focusing on this question type, 18 on multiple-choice, three on both freeresponse and multiple-choice questions, and one on verbal response questions. Some studies employ additional resources such as WordNet (Kwankajornkiet et al 2016;Kumar et al 2015a) or DBpedia (Faizan and Lohmann 2018;Faizan et al 2017;Tamura et al 2015) to generate distractors. By contrast, MCQs are more prevalent in studies using Semantic Web technologies, with ten studies focusing on the generation of multiple-choice questions and four studies focusing on free-response questions.…”

Section: Inputmentioning

confidence: 99%

See 2 more Smart Citations

A Systematic Review of Automatic Question Generation for Educational Purposes

Kurdi

Leo

Parsia

et al. 2019

Int J Artif Intell Educ

244

111

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While exam-style questions are a fundamental educational tool serving a variety of purposes, manual construction of questions is a complex process that requires training, experience, and resources. This, in turn, hinders and slows down the use of educational activities (e.g. providing practice questions) and new advances (e.g. adaptive testing) that require a large pool of questions. To reduce the expenses associated with manual construction of questions and to satisfy the need for a continuous supply of new questions, automatic question generation (AQG) techniques were introduced. This review extends a previous review on AQG literature that has been published up to late 2014. It includes 93 papers that were between 2015 and early 2019 and tackle the automatic generation of questions for educational purposes. The aims of this review are to: provide an overview of the AQG community and its activities, summarise the current trends and advances in AQG, highlight the changes that the area has undergone in the recent years, and suggest areas for improvement and future opportunities for AQG. Similar to what was found previously, there is little focus in the current literature on generating questions of controlled difficulty, enriching question forms and structures, automating template construction, improving presentation, and generating feedback. Our findings also suggest the need to further improve experimental reporting, harmonise evaluation metrics, and investigate other evaluation methods that are more feasible.

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Section: Generation Tasksmentioning

confidence: 99%

Section: Generation Tasksmentioning

confidence: 99%

Section: Inputmentioning

confidence: 99%

See 1 more Smart Citation

A Systematic Review of Automatic Question Generation for Educational Purposes

Kurdi

Leo

Parsia

et al. 2019

Int J Artif Intell Educ

244

111

View full text Add to dashboard Cite

show abstract

“…The Thai sentences rely on individual judgment when there are unknown words, while English sentences have natural spaces. Besides, Thai has structural ambiguities in which Part-of-Speech (POS) tags relied on context words that affect one word may have the opposite meaning [8,14,15,16,17]. Thus, data preprocessing has involved the analysis of Thai sentences.…”

Section: Text Preprocessingmentioning

confidence: 99%

“…In addition, POS tagging could help improve performance for the study of Thai texts. The most probable Part-of-Speech (POS) sequence aims to help determine the type of space for generating questions from Thai text proposed in [8].…”

Section: Text Preprocessingmentioning

confidence: 99%

Part-of-Speech tagging enhancement to natural language processing for Thai wh-question classification with deep learning

Chotirat¹,

Meesad²

2021

Heliyon

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Question classification is a crucial task for answer selection. Question classification could help define the structure of question sentences generated by features extraction from a sentence, such as who, when, where, and how. In this paper, we proposed a methodology to improve question classification from texts by using feature selection and word embedding techniques. We conducted several experiments to evaluate the performance of the proposed methodology using two different datasets (TREC-6 dataset and Thai sentence dataset) with term frequency and combined term frequency-inverse document frequency including Unigram, Unigram+Bigram, and Unigram + Trigram as features. Machine learning models based on traditional and deep learning classifiers were used. The traditional classification models were Multinomial Naïve Bayes, Logistic Regression, and Support Vector Machine. The deep learning techniques were Bidirectional Long Short-Term Memory (BiLSTM), Convolutional Neural Networks (CNN), and Hybrid model, which combined CNN and BiLSTM model. The experiment results showed that our methodology based on Part-of-Speech (POS) tagging was the best to improve question classification accuracy. The classifying question categories achieved with average micro 𝐹 1 -score of 0.98 when applied SVM model on adding all POS tags in the TREC-6 dataset. The highest average micro 𝐹 1 -score achieved 0.8 when applied GloVe by using CNN model on adding focusing tags in the Thai sentences dataset.

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