Automatic question-answer pairs generation and question similarity mechanism in question answering system

Aithal, Shivani G.; Rao, Ashish K.; Singh, Sanjay

doi:10.1007/s10489-021-02348-9

Cited by 27 publications

(12 citation statements)

References 35 publications

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“…The results show that sequential models such as LSTM and 1-D CNN work better on standard word order (SOV) and transformer-based models such as BERT language models composed of fully connected networks and attention mechanism works well for other word-order types, in the Persian language. As for future work, we can mention the use of generative methods to generate datasets [ 72 ]. In these methods, in addition to the translation and native datasets, an automated dataset produced by generative methods can be employed.…”

Section: Discussionmentioning

confidence: 99%

PerAnSel: A Novel Deep Neural Network-Based System for Persian Question Answering

Mozafari

Kazemi

Moradi

et al. 2022

Computational Intelligence and Neuroscience

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Question answering (QA) systems have attracted considerable attention in recent years. They receive the user’s questions in natural language and respond to them with precise answers. Most of the works on QA were initially proposed for the English language, but some research studies have recently been performed on non-English languages. Answer selection (AS) is a critical component in QA systems. To the best of our knowledge, there is no research on AS for the Persian language. Persian is a (1) free word order, (2) right-to-left, (3) morphologically rich, and (4) low-resource language. Deep learning (DL) techniques have shown promising accuracy in AS. Although DL performs very well on QA, it requires a considerable amount of annotated data for training. Many annotated datasets have been built for the AS task; most of them are exclusively in English. In order to address the need for a high-quality AS dataset in the Persian language, we present PASD; the first large-scale native AS dataset for the Persian language. To show the quality of PASD, we employed it to train state-of-the-art QA systems. We also present PerAnSel: a novel deep neural network-based system for Persian question answering. Since the Persian language is a free word-order language, in PerAnSel, we parallelize a sequential method and a transformer-based method to handle various orders in the Persian language. We then evaluate PerAnSel on three datasets: PASD, PerCQA, and WikiFA. The experimental results indicate strong performance on the Persian datasets beating state-of-the-art answer selection methods by 10.66 % on PASD, 8.42 % on PerCQA, and 3.08 % on WikiFA datasets in terms of MRR.

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Section: Discussionmentioning

confidence: 99%

PerAnSel: A Novel Deep Neural Network-Based System for Persian Question Answering

Mozafari

Kazemi

Moradi

et al. 2022

Computational Intelligence and Neuroscience

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“…This study uses systematic literature review as the methodological approach to assess and understand the pertinent literature linked with research questions. From a number of approaches available in the literature for systematic review of studies, this study uses the approach provided by the researchers in [16] who provided detailed guidelines for conducting reviews. The methodological review consists of three key stages which in turn included ten sub activities.…”

Section: Methodsmentioning

confidence: 99%

“…Later and to extend this work, the researchers in [3] proposed a system called QuizJET, as another tool for generating questions, especially for teaching Java. This QuizJET was based on template-based questions that was linked to different concepts of Java and was actively used for generating quizzes; however, the research conducted in [16] did not find any correlation between the success rates of QuizJET and students' home assignment work, because QuizJET was more focused on understanding programs while home assignments were focused on writing skills of computer programming learners. Despite all these, QuizJET was identified as a valuable source in preparing students for exams.…”

Section: B Automatic Question Generation In Teaching Programmingmentioning

confidence: 99%

“…Recently, the research article [15] provided facts for generating questions for teaching programming languages. The study reported in [16] found that students are taught by teachers to produce a program but they are not fully sure about the use of their own codes as they failed to grasp basic concepts. But if students are taught using Quality License Scheme (QLS,) it can prompt them to have a deeper level of understanding of given concepts.…”

Section: B Automatic Question Generation In Teaching Programmingmentioning

confidence: 99%

“…These are the following: 1) after COVID-19, the instructors valued code-writing question generation techniques like Junit that is a web-based tool and used user feedback as a validating tool. 2) Next, ITA [12], JAVA software, C programming for generating www.ijacsa.thesai.org questions, CodeTraining [15], dynamic codes [16] and PQG model [23] were employed by instructors for generating questions that were identified as the key source of positive ratings in the assessment of students during exams. 3) The analysis of articles published after COVID-19, i.e.…”

Section: A Comparison Of State-of-the-field Before Covid-19 and After...mentioning

confidence: 99%

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A Review of Automatic Question Generation in Teaching Programming

Alshboul¹,

Baksa-Varga²

2022

IJACSA

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Computer programming is a complex field that requires rigorous practice in programming code writing and learning skills, which can be one of the critical challenges in learning and teaching programming. The complicated nature of computer programming requires an instructor to manage its learning resources and diligently generate programming-related questions for students that need conceptual programming and procedural programming rules. In this regard, automatic question generation techniques help teachers carefully align their learning objectives with the question designs in terms of relevancy and complexity. This also helps in reducing the costs linked with the manual generation of questions and fulfills the need of supplying new questions through automatic question techniques. This paper presents a theoretical review of automatic question generation (AQG) techniques, particularly related to computer programming languages from the year 2017 till 2022. A total of 18 papers are included in this study. one of the goals is to analyze and compare the state of the field in question generation before COVID-19 and after the COVID-19 period, and to summarize the challenges and future directions in the field. In congruence to previous studies, there is little focus given in the existing literature on generating questions related to learning programming languages through different techniques. Our findings show that there is a need to further enhance experimental studies in implementing automatic question generation especially in the field of programming. Also, there is a need to implement an authoring tool that can demonstrate designing more practical evaluation metrics for students.

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Q&A Generation for Flashcards Within a Transformer-Based Framework

Thabet

Zanichelli

2023

Communications in Computer and Information Science

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Automatic question-answer pairs generation and question similarity mechanism in question answering system

Cited by 27 publications

References 35 publications

PerAnSel: A Novel Deep Neural Network-Based System for Persian Question Answering

PerAnSel: A Novel Deep Neural Network-Based System for Persian Question Answering

A Review of Automatic Question Generation in Teaching Programming

Q&A Generation for Flashcards Within a Transformer-Based Framework

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