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Automatic Question-Answer Generation from Video Lecture using Neural Machine Translation

Video lectures perform very crucial role in learning nowadays, from video or online lectures a learner can retrieve content or gain knowledge anytime and anywhere. The paper presents Question and Answer Generation from video or online lectures automatically, from such videos the learner can be able to self-assist himself. The main goal of the presented project is to convert video speech into text, and that text is applied for Question-and-Answer Generation Automatically after selecting only highly instructive sentences from the text. Conventional approaches have mainly used heuristic rules that convert a sentence into questions. The proposed Encoder-decoder neural network model which generates significant as well as different questions from the sentences. The encoder is used to read the input text with the position of answer. The output of encoder is fed to the decoder to generates question with answers using SQuAD dataset.