A Framework for Creating Text Parsing Dialogue Systems
Educational dialogue-based simulations have been used in variety of fields. Some applications include: teaching medical students effective communication methods for clinical exams , exposing military personnel to local culture before deployment , helping students practice a foreign language , and teaching negotiation strategies . Many of these simulations use a multiple-choice dialogue system to simulate conversation. User input can easily be scored based on the educational goal of the simulation because each multiple-choice option has an associated score. Despite its advantages, multiple-choice training is a poor proxy for the complexity of real-word interactions. Free-input training would be more appropriate, but free-input dialogue systems are difficult to implement and do not offer a clear input scoring mechanism. Many past dialogue-based simulations with a free-input dialogue system have restricted input to specific phrases that users are taught during a training phase . Other dialogue-based simulations have used a non-restrictive free-input dialogue system but lack an educational mechanism because they do not perform detailed classification of specific text features in the input to provide a score .
The goal of this research is to develop a framework for creating a free-input dialogue system that is able to perform detailed classification of input to provide educational feedback. Components of the developed framework include a data generation scheme for creating potential input, methods for creating crowdsourcing prompts for collecting data, and a classification pipeline that finds machine learning models and text representations to create classification models for scoring user input. The framework was implemented in an existing dialogue-based simulation to replace its multiple-choice dialogue system. Modeling results show that this approach offers a viable method for building a free-input dialogue system for an educational simulation.
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Dr. Donald Brown
Dr. Laura Barnes
Dr. Stephanie Guerlain