Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/166586
Title: AI for human-computer interaction
Authors: Kyaw, Htet Aung
Keywords: Engineering::Computer science and engineering
Issue Date: 2023
Publisher: Nanyang Technological University
Source: Kyaw, H. A. (2023). AI for human-computer interaction. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/166586
Project: PSCSE21-0006 
Abstract: The use of chatbots to have conversational interactions with users is growing in popularity. They are used for a variety of purposes, such as information retrieval, customer service, and entertainment. In this project, we analyze how to create a chatbot that can converse with users in a natural and the appropriate manner by utilizing the PyTorch deep learning library. We start by gathering a large amount of conversational data and pre-processing it by cleaning, filtering, and tokenizing it. The PyTorch library is then used to train a neural network model based on a sequence-to-sequence architecture with an attention mechanism. The model takes a sequence of tokens representing a user's message as input and produces a sequence of tokens representing the chatbot's response. We use both automatic metrics and human evaluation through user studies to evaluate the performance of our chatbot. Our results show that our chatbot can respond to user input in a coherent and contextually appropriate manner, outperforming several baseline models. Our model can also handle long and complex conversations, demonstrating resilience in maintaining context across multiple turns. In addition to evaluating our model's performance, we explore the effect of various model configurations, such as the number of layers, hidden size, and attention mechanism. We discovered that increasing the model's complexity can lead to better performance, but it also increases training time and demands more data to avoid overfitting. Overall, our findings show that PyTorch can be used to build conversational agents and highlight the potential for further improvements in chatbot performance using advanced deep learning techniques. Our work also adds to the growing research on building chatbots with deep learning methods, laying the groundwork for future work in this area.
URI: https://hdl.handle.net/10356/166586
Schools: School of Computer Science and Engineering 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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