AIML and sequence-to-sequence models to build artificial intelligence chatbots: insights from a comparative analysis

A chatbot is a software that is able to autonomously communicate with a human being through text and due to its usefulness, an increasing number of businesses are implementing such tools in order to provide timely communication to their clients. In the past, whilst literature has focused on implementing innovative chatbots and the evaluation of such tools, limited studies have been done to critically comparing such conversational systems. In order to address this gap, this study critically compares the Artificial Intelligence Mark-up Language (AIML), and Sequence-to-Sequence models for building chatbots. In this endeavor, two chatbots were developed to implement each model and were evaluated using a mixture of glass box and black box evaluation, based on 3 metrics, namely, user’s satisfaction, the information retrieval rate, and the task completion rate of each chatbot. Results showed that the AIML chatbot ensured better user satisfaction, and task completion rate, while the Sequence-to-Sequence model had better information retrieval rate.

This is a post-peer-review, pre-copyedit version of a paper published in Smart and Sustainable Engineering for Next Generation Applications. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-18240-3_30

Paper published as: Teckchandani N., Santokhee A., Bekaroo G. (2019) AIML and Sequence-to-Sequence Models to Build Artificial Intelligence Chatbots: Insights from a Comparative Analysis. In: Fleming P., Lacquet B., Sanei S., Deb K., Jakobsson A. (eds) Smart and Sustainable Engineering for Next Generation Applications. ELECOM 2018. Lecture Notes in Electrical Engineering, vol 561. Springer, Cham