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Dental TutorBot: Exploitation of Dental Textbooks for Automated Learning

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Dental TutorBot:
Exploitation of
Dental Textbooks
for Automated
Learning

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Introduction
1.

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What the
paper is
about?
An open-source Dental Chatbot
Tutor system, trained on
academic books pertaining to
Dental Surger...

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Dental TutorBot: Exploitation of Dental Textbooks for Automated Learning

  1. 1. Dental TutorBot: Exploitation of Dental Textbooks for Automated Learning
  2. 2. Introduction 1.
  3. 3. What the paper is about? An open-source Dental Chatbot Tutor system, trained on academic books pertaining to Dental Surgery using AI models. The Chatbot asks short questions and evaluates responses from medical students to assess their academic level, while providing hints to them as well in order to help them connect concepts and theories during attempts.
  4. 4. Lack of Physical Correspondence and Accessibility to educational stakeholders, resources has rocketed due to the on-going pandemic, Remote Learning is an exponentially-growing success story, where everyone has a chance to learn and grow, Social Constructs, Barriers no longer as massive an issue for potential students, Theory-reliant practice – changes to underlying content on the occasional side, Often free or lower-priced, in comparison to traditional education, The medical field in general appears to be more reliant on traditional didactic approaches when it comes to teaching, ….and many other benefits! Why is this needed?
  5. 5. TutorBot Opportunities Leveraging Innovative Self-paced Learning in Dental Education has become highly desired. Virtualization of Education using TutorBots trained on Dental Education content can help bridge this gap.
  6. 6. Methodology 2.
  7. 7. A Conglomeration of Problems Text Pre- processing and Transformation Question, Answer Generation Data Ingestion – API Connectors, Databases RASA’s Intermediate Dialog Flow and Integration Analyzing Student Engagement Data Dental Nomenclature Validation Data Modeling and Storage WhatsApp Integration Hint Generation Topic Generation
  8. 8. A high-grade replicating implementation such as the following is essentially needed: An Intelligent TutorBot’s Reasoning Flow Intent Classifier Domain Model Tutor Model (Next Best Action) Student Response Analyzer (SRA) Learner Model Topic Mastery Progress Update Student Utterance Valid Answer Help / Hint / Question Mastery Update Knowledge Assessment
  9. 9. Question Answer Generation 2.1.
  10. 10. Answer-Aware Question Generation, using multi-task model architype for better performance: Basis: Cleaned Dental Textbooks and SQuADv1 Dataset, Google T5 Model (Text- to-Text Transfer Transformer) and Generative Pre-trained Transformer (GPT-2) Combination. Emphasis: seq2seq Encoder-Decoder Framework. Tests: Dental Textbooks Data. Safety-net Confidence: Personal Dentist. QA Generator Pipeline Span-based Answer Extraction Question Generation based on Answers Question Answering
  11. 11. Topic Extraction and Modeling 2.2.
  12. 12. The same sample text utilized for the QA Generation task was fed to a Latent Dirichlet Allocation (LDA) model implementation LDA – a generative statistical method that allows sets of observations to be explained in terms of groups which explain why some parts of the data are similar. For example, if observations are words collected into documents, it operates on the theory that: 1. Each document is a mixture of a small number of topics. 2. Each word's presence is attributable to one of the document's topics. Topic Extraction Pipeline
  13. 13. Perform Lemmatization, Stemming and Stopword Pre-Processing Split text to create corpora Using a generated dictionary, each ‘document’ is converted into a BoW model Based on BoW Histograms, LDA computes Dirichlet prior probability of each word with class parameters reliant on document-wise and word-wise distributions This computation allows weighting on each word occurrence relative to its count, thus the frequently occurring words begin to appear as natural topic selections Topic Extraction Pipeline Pre-Processing Bag-of-Words LDA Topic Weights, Groups
  14. 14. Topical Groups Word Cloud
  15. 15. Topical Weights
  16. 16. LDA Visualization
  17. 17. Hint Generation 2.3.
  18. 18. We address the “hint” as a semantic chunk in an input passage that will be included (or rephrased) in the target question and the accompanied answer. Based on this definition, we perform syntactic parsing and chunking on input text, and select chunks which are the most relevant to the target question as the clue set. Leverage two sub-systems for the task (inclusive of Word Embedding): A: Lexical Simplification using WordNet, BabelNet and Rake reliant on Text Summarization, Expansion, Keyword Similarity and Rephrasing B: GPT-2-small model trained on Semantic Clues learned from SQuAD using above pipeline Hint Generation Pipeline
  19. 19. Candidate Chunks Tokenizing, Stemming Compute Score for Maximum Overlapping tokens, stems
  20. 20. Results 3.
  21. 21. Sample from Dental Text: QA, Topic Results:
  22. 22. Evaluative Feedback on Generated Data
  23. 23. Evaluative Feedback on Generated Data
  24. 24. Hints by System A Hints by System B
  25. 25. Data Modeling and Storage 4.
  26. 26. Samples of Generated Data
  27. 27. The following tables were maintained in order to fulfill each User - Bot paradigm: Database Schema ID Conversation ID Bot Message User Message Expected User Message Confidence Visited User, Bot Interactions ID Question Answer Hint Question Type Difficulty Level Topic Category Provided QAs ID Question Answer Relevancy Rank Reason Generated QAs ID Question Type Question Type ID Topic Category Topic Type ID Difficulty Level Difficulty Type ID TutorBot Session No User No Conversation Status Conversation Credentials ID Name Number Education User Credentials
  28. 28. Module Integration 5.
  29. 29. Basic Test Mock-up with TutorBot
  30. 30. Basic RASA Integration with TutorBot
  31. 31. WhatsApp Integration with TutorBot
  32. 32. Final Testing for TutorBot
  33. 33. TutorBot Framework
  34. 34. Future Work 6.
  35. 35. Improvised Hint Generation, Question Rating Generation, Human Evaluation (Students, Dentists, Teachers), Learning and Enabling Objectives, In-class Piloting, Authentic Response Control – Instructor vs. TutorBot, Ontology Exploitation, Extensive Deployment and Correlative Study, Real-time Training and Testing, Possible Upgrades in the Pipeline ….and many other possibilities!
  36. 36. Utilizing Ontologies
  37. 37. End Result? A reliable platform advocating and positively exploiting the uses and benefits of virtual education while capsizing the disadvantages of physical education, that can influence many other sub-and- super systems to follow suit.
  38. 38. References • https://expertsystem.com/chatbot • https://arxiv.org/pdf/1111.4343.pdf • https://medium.com/lingvo-masino/question-and-answering-in- natural-language-processing-part-i-168f00291856 • https://medium.com/voice-tech-podcast/the-9-best-chatbot- development-frameworks-f034be1ff53c • https://www.ubuntupit.com/best-chatbot-frameworks-to-build- powerful-ai-bots/
  39. 39. References • https://towardsdatascience.com/nlp-building-a-question-answering- model-ed0529a68c54 • Reddy, S., Chen, D., & Manning, C. D. (2019). Coqa: A conversational question answering challenge. Transactions of the Association for Computational Linguistics, 7, 249-266 • Mishra, A., & Jain, S. K. (2016). A survey on question answering systems with classification. Journal of King Saud University-Computer and Information Sciences, 28(3), 345-361
  40. 40. References • Dialogue-based tutoring at scale: Design and Challenges • Developing a Large-scale Feedback System to Train Dialogue-based Tutors using Student Annotations • Preliminary Evaluations of a Dialogue-Based Digital Tutor • The Personality of AI Systems in Education: Experiences with the Watson Tutor, a one-on-one virtual tutoring system
  41. 41. CREDITS: This presentation template was created by Slidesgo, including icons by Flaticon, and infographics & images by Freepik. Thank You! Please keep this slide for attribution.

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