Abstract
This paper presents a learning assistant that tests one’s knowledge and gives feedback that helps a person learn at a faster pace. A learning assistant (based on automated question generation) has extensive uses in education, information websites, self-assessment, FAQs, testing ML agents, research, etc. Multiple researchers, and companies have worked on Virtual Assistance, but majorly in English. We built our learning assistant for Telugu language to help with teaching in the mother tongue, which is the most efficient way of learning. Our system is built primarily based on Question Generation in Telugu. Many experiments were conducted on Question Generation in English in multiple ways. We have built the first hybrid machine learning and rule-based solution in Telugu, which proves efficient for short stories or short passages in children’s books. Our work covers the fundamental question forms with question types: adjective, yes/no, adverb, verb, when, where, whose, quotative, and quantitative (how many/how much). We constructed rules for question generation using Part of Speech (POS) tags and Universal Dependency (UD) tags along with linguistic information of the surrounding relevant context of the word. We used keyword matching, multilingual sentence embedding to evaluate the answer. Our system is primarily built on question generation in Telugu, and is also capable of evaluating the user’s answers to the generated questions.
Highlights
Research on Virtual Assistants is renowned since they being widely used in recent times for numerous tasks
Based on the observation of the data chosen and analysis of all the possible causes, we developed a set of rules for each part of speech that can be formed into a question word in Telugu
We built rules for question generation based on Part of Speech (POS) tags, Universal Dependency (UD) tags and information surrounding the word, which is comparable with Vibhaktis in Telugu grammar
Summary
Research on Virtual Assistants is renowned since they being widely used in recent times for numerous tasks. These assistants are generated using large datasets and high-end Natural Language Understanding (NLU) and Natural Language Generation (NLG) tools. Research is still going on to make these assistants work in major Indian languages as well. An automated learning assistant like our system is useful for the learning process for humans and for machines in the process of testing ML systems. Research has been done for Question Answer generating system in English, concentrating on basic Wh-questions with a rule-based approach, question template based approaches etc. For a low-resourced language like Telugu, a complete AI-based solution can be non-viable. A completely rule-based system might leave out principle parts of the abstract.
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