Xiao-Shih: A Self-Enriched Question Answering Bot With Machine Learning on Chinese-Based MOOCs
Hao-Hsuan Hsu, Nen‐Fu Huang
Abstract
This article introduces Xiao-Shih, the first intelligent question answering bot on Chinese-based massive open online courses (MOOCs). Question answering is critical for solving individual problems. However, instructors on MOOCs must respond to many questions, and learners must wait a long time for answers. To address this issue, Xiao-Shih integrates many novel natural language processing and machine learning approaches to achieve state-of-the-art performance. Furthermore, Xiao-Shih has a built-in self-enriched mechanism for expanding the knowledge base through open community-based question answering. This article proposes a novel approach, known as spreading question similarity (SQS), which iterates similar keywords on our keyword networks to find duplicate questions. Compared with BERT, an advanced neural language model, the results showed that SQS outperforms BERT on recall and accuracy above a prediction probability threshold of 0.8. After training, Xiao-Shih achieved a perfect correct rate. Furthermore, Xiao-Shih outperforms Jill Watson 1.0, which is a noted question answering bot, on answer rate with the self-enriched mechanism.