Automatic Question Generation System Research Articles

Comparison of template-based and multilayer perceptron-based approach for automatic question generation system

Intelligent tutoring systems are computer-assisted learning systems with adaption to students using artificial intelligence tools. An intelligent tutoring system can drastically improve education efficiency as it provides solutions to many issues that now plague the educational industry. One important component in education is questioning learners to assess and reinforce learning. This research compares two approaches for automatic question generation, a template-based question generation strategy and the phrase-Level automatic question generation system utilizing the Multilayer perceptron model. A template-based technique is a baseline for automatic question generation that uses templates taken from the training set to generate questions by filling certain templates with specific topic items. We utilize question-answer sentence composition datasets and manually constructed datasets for our experiments and comparison of the Multilayer perceptron training model. We used both human and automatic evaluation metrics to assess the efficiency of our suggested methods. Regarding automatic metrics, we selected the BLEU-n gram and ROUGE-N methodologies. The evaluation results demonstrate that the phrase-level Multilayer perceptron-based strategy dominates the template-based approach and has a promising score in both ROUGE automatic and human evaluation metrics.

Automatic Question Generator System Conceptual Model for Mathematic and Geometry Parallel Question Replication

Weaknesses in the paper assessment process have been able to overcome using the Computer Assisted Assessment (CAA) objective question. The weakness that can be overcome is the reduction in time for correction and the process of handling thousands of participants simultaneously. However, the process of preparing objective questions is still constrained by providing parallel questions. Parallel questions are made so that each examinee gets a different question but has the same level of difficulty. Making parallel questions manually requires expensive costs and the consistency of the difficulty level must be the same. Some researchers propose an Automatic Question Generation (AQG) system to overcome these problems. In this study an AQG system will be developed to make parallel questions for the material tested at each school level. The process of previous generating questions that can only detect text and numeric variables, will be developed so that they can detect image variables and can detect geometry types based on numerical variables. The system is expected to work like humans who can make questions when given a text, numeric, image, or mathematical notation. By combining the Stem, Multi Part Parser Algorithm and the Custom Media Type Parser Algorithm in the Python-Django framework, the system can provide question recommendations according to mathematical geometry-based input. The process of generating items in this study will use the concept of manipulation of keywords represented by variables. This variable stores values in the form of text, numeric, and image. The ability to manipulate text, numerics, and images is applied to the developed AQG system so that it can be used to generate questions on the material to be tested. AQG is expected to be able to detect text, numeric, notation, and image input and produce output in the form of geometry-based mathematical problems according to text, numeric, notation, and image input with a better level of accuracy.

Automated Question Extraction and Tagging for Cloud based Online Communities

Crowd-based question answering forums and cloud-based community question answering platforms provide us with the dais to post questions and answers online. This helps the users to get desired answers from expert users. It is a challenge for a person with mobility needs to go out and explore. The 'wheelchair accessible' information provided by Google Maps is useful to explore before going out. Local guides share this knowledge on Google Maps by answering quick questionnaire. Automated question generation is a key challenge that we face with regard to natural languages in the context of users visited locations, already reviewed places, likes, interests and user experience. In this paper, we have implemented an automatic question generation system that comprises of part-of-speech (POS) tagger, text-to-question generation task using syntactic analysis and a named entity extraction. The proposed system is tested with human effort and is generating valid questionnaire.

Automated question extraction and tagging for cloud-based online communities

Crowd-based question answering forums and cloud-based community question answering platforms provide us with the dais to post questions and answers online. This helps the users to get desired answers from expert users. It is a challenge for a person with mobility needs to go out and explore. The 'wheelchair accessible' information provided by Google Maps is useful to explore before going out. Local guides share this knowledge on Google Maps by answering quick questionnaire. Automated question generation is a key challenge that we face with regard to natural languages in the context of users visited locations, already reviewed places, likes, interests and user experience. In this paper, we have implemented an automatic question generation system that comprises of part-of-speech (POS) tagger, text-to-question generation task using syntactic analysis and a named entity extraction. The proposed system is tested with human effort and is generating valid questionnaire.

Controlling item difficulty for automatic vocabulary question generation

The present study investigates the best factor for controlling the item difficulty of multiple-choice English vocabulary questions generated by an automatic question generation system. Three factors are considered for controlling item difficulty: (1) reading passage difficulty, (2) semantic similarity between the correct answer and distractors, and (3) the distractor word difficulty level. An experiment was conducted by administering machine-generated items to three groups of English learners. The groups were determined based on their standardised English test scores. In total, 120 items, generated using combinations of the above three factors, were tested. The results reveal that the distractor word difficulty level had the greatest impact on item difficulty, but this tendency changed depending on the proficiency of the test takers. These results will be of use when implementing a fully automatic system for administrating tests.

Evaluation of automatically generated English vocabulary questions

This paper describes details of the evaluation experiments for questions created by an automatic question generation system. Given a target word and one of its word senses, the system generates a multiple-choice English vocabulary question asking for the closest in meaning to the target word in the reading passage. Two kinds of evaluation were conducted considering two aspects: (1) measuring English learners’ proficiency and (2) their similarity to the human-made questions. The first evaluation is based on the responses from English learners obtained through administering the machine-generated and human-made questions to them, and the second is based on the subjective judgement by English teachers. Both evaluations showed that the machine-generated questions were able to achieve a comparable level with the human-made questions in both measuring English proficiency and similarity.

Tapping into the Power of Automatic Question Generation

ABSTRACT Question Generation (QG) is an important element of learning environments, information seeking systems, help systems, and other applications. There are a number of distinct research subfields which are concerned with the Automatic Question Generation (AQG) Systems. This research tries to have a wide look on existing automatic question generation systems, and some trials of overcoming its difficulties from different points of views . General Terms Natural Language Processing, Natural Language Generation questions) whereas sha Keywords Automatic Question Generation, Questions Taxonomy, Multiple Choice Questions, Entity Based Questions. 1. INTRODUCTION Questions have been studied as part of the task of Question Answering in the field of Natural Language Processing (NLP). At the beginning, question answering research focused on answering questions from databases and knowledge representations [1], but in the past two decades has refocused on retrieving answers from text – e.g., in 1999 the evaluation of question-answering systems became part of the Text Retrieval Conference (TREC) series. Simultaneously, there has been a strand of research on advisory dialogue systems [2]. All the previous systems were primarily aimed at responding to the user’s questions. Recently, there has been a broader transformation in the field of Natural Language Processing researches in Question Generation task. Since 2008, researchers from different communities, such as, Discourse Analysis, Dialogue Modeling, Formal Semantics, Intelligent Tutoring Systems, Natural Language Generation, Natural Language Understanding, and Psycholinguistics, have met annually at the Question Generation workshop. AQG system would be useful for building an automated trainer for learners to ask better questions, and for building a better hint and question asking facilities in intelligent tutoring systems [3]. Another benefit of QG is that it can be a good tool to help in improving the quality of the Question Answering (QA) systems. Available studies revealed that humans were not very skilled in asking good questions. Therefore, they would benefit from automated QG systems to assist them in meeting their inquiry needs [4]. In this research, a survey about automatic question generation and different variety of work that has been done is discussed. The rest of the paper is organized as follows: section 2 discusses the basic steps for automatic question generation systems; section 3 introduces two distinct taxonomies of questions, section 4 the state of the art in which a description of the previous work, finally section 5 introduces a conclusion with some remarks.

Functional Modules of a Semi-Automatic Question Generating System

In the last decade different applications of computer science (data mining, text mining) have been recognised. Presently an automatic question generation is a current research area. The elaborated system generates multi-choice questions from textbooks without using an external semantic database. Main demands made for the implemented automatic question generation system: supporting the Hungarian language, using a free grammar analytic, generating test questions from annotated texts, implementing potential answers from an internal, own developed semantic data base, supporting multi-choice problems, a possibility of implementing electronic and printable test papers. During planning the framework among the applied methods, clustering on the base of distances, a classification algorithm on the base of a neural net, in the case of generating questions and answer alternatives an own developed semantic data base was implemented. To prove the practical applicability of the automated question generating method, Java-language software was developed.

The Development of an Automatic Question Generation System on Facebook Using an Artificial Bee Colony Algorithm

Facebook is currently one of the world's most popular social networking services, and has been widely used in the field of e-learning. In general, learners in e-learning environments need to evaluate their learning ability through taking tests to present the learning achievement. In order to evaluate their ability on e-learning platform with social network services, this study proposes an automatic question generation system for individual learning status. The proposed system uses the artificial bee colony algorithm to find suitable questions for each learner according to the learner's profile, reading experience, professional ability, and the e-learning records in the system. The experimental results indicate that the proposed method improves the accuracy of the automatic question generation system and that it outperforms the random method.

G-Asks: An Intelligent Automatic Question Generation System for Academic Writing Support

Many electronic feedback systems have been proposed for writing support. However, most of these systems only aim at supporting writing to communicate instead of writing to learn, as in the case of literature review writing. Trigger questions are potentially forms of support for writing to learn, but current automatic question generation approaches focus on factual question generation for reading comprehension or vocabulary assessment. This article presents a novel Automatic Question Generation (AQG) system, called G-Asks, which generates specific trigger questions as a form of support for students' learning through writing. We conducted a large-scale case study, including 24 human supervisors and 33 research students, in an Engineering Research Method course at The University of Sydney and compared questions generated by G-Asks with human generated question. The results indicate that G-Asks can generate questions as useful as human supervisors (`useful' is one of five question quality measures) while significantly outperforming Human Peer and Generic Questions in most quality measures after filtering out questions with grammatical and semantic errors. Furthermore, we identified the most frequent question types, derived from the human supervisors' questions and discussed how the human supervisors generate such questions from the source text.