  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.
