Electronic Thesis and Dissertation Repository

Thesis Format

Monograph

Degree

Master of Science

Program

Computer Science

Collaborative Specialization

Artificial Intelligence

Supervisor

Katchabaw, Michael

Abstract

In this thesis, we aim to contribute to ongoing research in the field of human- computer dialogue and help move closer to the goal of having more realistic human-computer dialogue. We address the current challenge of topic handling in human-computer conversation by proposing a Topic Handler model that is designed in such a way that is flexible and compatible with third party dialogue systems. This model builds off of previously proposed dialogue grammars and systems and is based on speech act theory and conversation analysis. By employing feature engineering of existing dialogue act corpora and using this data in machine learning experimentation, we successfully demonstrate that not only does speech act and semantic annotation data improve the performance of classifiers for the task of identifying appropriate points of topic change, we also demonstrate how the proposed Topic Handler model can provide needed inputs to assist in topic handling when used in parallel with dialogue systems.

Summary for Lay Audience

In this thesis, we aim to contribute to ongoing research in the field of human-computer dialogue and help move closer to the goal of having more realistic human-computer dialogue. We address the current challenge of topic handling in human-computer conversation by proposing a Topic Handler model that is designed in such a way that is flexible and compatible with third party dialogue systems. This model builds off of previously proposed dialogue grammars and systems and is based on linguistic methods and theories (e.g. speech act theory and conversation analysis). By leveraging pre-annotated corpora and further creating additional input features for machine learning experimentation, we successfully demonstrate that not only does speech act and semantic annotation data improve the performance of machine learning models for the task of identifying appropriate points of topic change, we also demonstrate how the proposed Topic Handler model can provide needed inputs to assist in topic handling when used in parallel with dialogue systems.

Available for download on Wednesday, May 31, 2023

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