TY  - JOUR
AU  - Mustapha, Aida 
AU  - Sulaiman, Md. Nasir 
AU  - Mahmod, Ramlan 
AU  - Selamat, Mohd. Hasan 
PY  - 2010
TI  - Dynamic Bayesian Networks in Classification-and-Ranking Architecture of Response Generation
JF  - Journal of Computer Science
VL  - 7
IS  - 1
DO  - 10.3844/jcssp.2011.59.64
UR  - https://thescipub.com/abstract/jcssp.2011.59.64
AB  - Problem statement: The first component in classification-and-ranking architecture is a
Bayesian classifier that classifies user utterances into response classes based on their semantic and
pragmatic interpretations. Bayesian networks are sufficient if data is limited to single user input
utterance. However, if the classifier is able to collate features from a sequence of previous n-1 user
utterances, the additional information may or may not improve the accuracy rate in response
classification. Approach: This article investigates the use of dynamic Bayesian networks to include
time-series information in the form of extended features from preceding utterances. The experiment
was conducted on SCHISMA corpus, which is a mixed-initiative, transaction dialogue in theater
reservation. Results: The results show that classification accuracy is improved, but rather
insignificantly. The accuracy rate tends to deteriorate as time-span of dialogue is increased.
Conclusion: Although every response utterance reflects form and behavior that are expected by the
preceding utterance, influence of meaning and intentions diminishes throughout time as the
conversation stretches to longer duration.