The design and implementation of Language Learning Chatbot with XAI using Ontology and Transfer Learning

TL;DR

This paper presents a transfer learning-based English language learning chatbot that uses GPT-2 and ontology graphs to provide explainable, multi-level language learning interactions, integrated into WeChat.

Contribution

It introduces a novel multi-level language learning framework with ontology-based explanations for GPT-2 outputs, enhancing transparency and educational effectiveness.

Findings

Effective phonetics and pronunciation correction.

Ontology-based explanations improve transparency.

Successful integration into WeChat platform.

Abstract

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.