The aim of the study is to contribute to research in the field of maintaining long-term engagingness in chatbots, which is done through rapport building with the help of user and agent specific memory. Recent advances in end-to-end trained neural conversational models (fully functional chit-chat chatbots created by training a neural model) present chatbots that converse well with respect to context understanding with the help of their short-term memory. However, these chatbots do not consider long-term memory, which in turn motivates further research. In this study, short-term memory is developed to allow the chatbot to understand con-text, such as context-based follow-up questions. Long-term memory is developed to re-member information between multiple interactions, such as information about the user and the agent’s own persona/personality. By introducing long-term memory, the chatbot is able to generate long-term memory-based questions, and to refer to the previous conversation, as well as retain a consistent persona. A question answering chatbot and question asking chatbot were initially developed in parallel as individual components and finally integrated into one chatbot system. The question answering chatbot was built in python and consisted of three main components; a generative model using GPT-2, a template structure with a related sentiment memory, and a retrieval structure. The question asking chatbot was built using a framework called Rasa. User tests were performed to primarily measure perceived engagingness and realness. The aim of the user studies was to compare performance between three chatbots; a) individual question asking, b) individual question answering and c) the integrated one. The results show that chatbots perceived as more human-like are not necessarily more engaging conversational partners than chatbots with lower perceived human-likeness. Although, while still not being near human level performance on measures such as consistency and engagingness, the developed chatbots achieved similar scores on these measures to that of chatbots in a related task (Persona-Chat task in ConvAI2). When measuring the effects of long-term memory in question asking, it was found that measures on perceived realness and persona increased when the chatbot asked long-term memory generated questions, referring to the previous interaction with the user.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-171927 |
| Date | January 2020 |
| Creators | Lundell Vinkler, Mikael, Yu, Peilin |
| Publisher | Linköpings universitet, Medie- och Informationsteknik, Linköpings universitet, Tekniska högskolan, Linköpings universitet, Medie- och Informationsteknik, Linköpings universitet, Tekniska högskolan |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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