International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 01 | Jan 2024
p-ISSN: 2395-0072
www.irjet.net
Enhancing Video Understanding: NLP-Based Automatic Question Generation JAGAN G, AKILA K Department of Computer Science and Engineering, SRM Institute of Science and Technology, Vadapalani, Chennai, Tamil Nadu, India -------------------------------------------------------------------------***-----------------------------------------------------------------------Chabot, assist with intelligent teaching, and even support Abstract - The goal of the abstract project is to improve self-learning tutorials by generating questions for testing and training purposes.
video comprehension using an autonomous question creation system that is NLP-based. The system creates pertinent and contextually appropriate inquiries based on the content of a particular video using NLP (Natural Language Processing) techniques. This procedure entails transcribing the audio from the film, identifying the main ideas, and creating inquiries that speak to various facets of the material. The produced questions can help students interact with the content of the video more deeply, foster critical thinking, and act as a tool for summarizing the topic. This study demonstrates the potential of fusing NLP with video content to offer a more dynamic and thorough learning experience by bridging the gap among visual knowledge and textual comprehension. An ongoing research trend in natural language processing is automatic question generation (AQG). AQG is very beneficial for computer-assisted assessments since it lowers the cost of manually creating questions and meets the demand for a steady stream of fresh questions. The majority of examstyle questions produced by automated question generation are of the "WH" ("What," "Who," and "Where" types) or reading comprehension variety. Based on their degrees of assessment, the questions must be varied or semantically distinct, although the responses may stay the same, in order to be as natural or human-like as possible. As a result, creating different sequences as part of question production has emerged as a crucial NLP activity, particularly in the publishing and education sectors.
Our primary goal in this research is to create question pairs for educational purposes. We are motivated by the fact that manually creating questions is quite tedious and time-consuming. Additionally, based on the evaluation levels, students need to be exposed to inquiries for a certain topic that range in difficulty [7]. For this reason, numerous studies have been conducted by researchers to produce a variety of queries that are semantically distinct from one another from a single source. For instance, handled a scenario of this nature using an encoderdecoder approach. This model is one of the most well-liked machine learning models and has attained an accuracy that, in some situations, even exceeds that of humans. When creating a variety of questions with the same response for the creation of various target sequences, Cho et al. suggested focused content selection in addition to a traditional encoder-decoder architecture. They employ the Stanford Question Dataset for training, testing, and data validation, along with several other AQG studies [10]. However, when trying to apply these methodologies in real-world circumstances, the inference-drawing process is not as straightforward as it may be. The data must always be in a particular format to be used with Squad or any other common dataset. For instance, the training set for Squad contains a few words, a question, and the appropriate response. This restriction is addressed by our model [4]. Our model's architecture is built to automatically generate inquiries from any text minus the need for manual intervention, making the process of testing and assessing the output fluid and approachable.
Keywords - NLP, AQG, Extract Audio, STT.
I. INTRODUCTION Natural Language Processing, a field of study that has been active over the past few decades, is responsible for the task of question generation. It seeks to produce inquiries from a given passage of text when the passage itself contains the answers to the Questions. There are many potential and beneficial uses for automatic question generation across several industries. A significant area of use for AQG is within the publishing and education sectors, where it has the potential to significantly improve tasks for both teachers and their pupils [5]. Not only that, but AQG can also support conversations between agents or
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II. LITERATURE REVIEW Ragasudha, I., & Saravanan, M. (2021, March) et al. Examination is important everywhere in the world. Exams are the basic approach used to gauge a student's knowledge and aptitude. A teacher is required to develop sets of exam questions in accordance with the institutions or universities. The preparation of the question paper is difficult for the teacher and requires a lot of their time.
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