Date of Award
Spring 6-17-2025
Degree Type
Thesis
Degree Name
Master of Science (MS)
School
School of Computing
First Advisor
Casey Bennett
Second Advisor
Peter Hastings
Third Advisor
David Hubbard
Abstract
This research address a key challenge in dialogue system: enabling the proactive, human-like shifting using lightweight approaching using MobileBERT (~25M) model was proposed and fine-tuned for topic shift detection, augmented with liguistic featuers for for topic trigger detection. Despite its smaller size (~25M parameters), the MobileBERT-based system achieved competitive results (F1 = 74.16%,) compared to the much larger XLNet model (~110M parameters, F1 = 79.95%), while offering greater efficiency. The topic trigger module, combining MobileBERT with linguistic features, further demonstrated effective performance (F1 = 71.61%).
Recommended Citation
Perumandla, Rohith, "Topic shift detection and triggering in natural dialogue systems: a lightweight approach" (2025). College of Computing and Digital Media Dissertations. 72.
https://via.library.depaul.edu/cdm_etd/72