The design and implementation of Language Learning Chatbot with XAI using Ontology and Transfer Learning
This work addresses the need for explainable and structured language learning tools, though it appears incremental by combining existing methods like transfer learning and XAI in a specific domain.
The authors developed an English language learning chatbot using transfer learning with GPT-2, incorporating three learning levels (phonetics, semantic, and free-style conversation) and an ontology graph for explainable AI (XAI) to visualize and interpret model outputs.
In this paper, we proposed a transfer learning-based English language learning chatbot, whose output generated by GPT-2 can be explained by corresponding ontology graph rooted by fine-tuning dataset. We design three levels for systematically English learning, including phonetics level for speech recognition and pronunciation correction, semantic level for specific domain conversation, and the simulation of free-style conversation in English - the highest level of language chatbot communication as free-style conversation agent. For academic contribution, we implement the ontology graph to explain the performance of free-style conversation, following the concept of XAI (Explainable Artificial Intelligence) to visualize the connections of neural network in bionics, and explain the output sentence from language model. From implementation perspective, our Language Learning agent integrated the mini-program in WeChat as front-end, and fine-tuned GPT-2 model of transfer learning as back-end to interpret the responses by ontology graph.