Exploring question generation in medical intelligent system using entailment

Abstract

The concept of a medical intelligent system has steadily garnered attention as modern technology advances. An intelligent medical system is a medical system that develops a certain amount of intelligence and performs a task without the assistance of a human. A chatbot can be thought of as a medical intelligent consultation system. The chatbot's question generation quality can be improved by creating more relevant questions depending on the patient's demands. Question generation, in addition to chatbots, is used to assess a learner's comprehension. This paper proposes a two-step approach to question generation. The first stage generates the entailment for the sentence that the question should be generated for. The generated entailed sentences are used to create questions in the second step. By generating questions from the original sentence, one can discover relevant information about the sentence. Furthermore, to increase the size of the entailment dataset, a data augmentation approach is used in this paper. The proposed work in this paper focuses on the importance of entailment in question generation and also studies the influence of entailment on the questions generated. Since data augmentation is employed, the overall effectiveness of data augmentation on the model is also investigated.