An Improved Approach of Intention Discovery with Machine Learning for
POMDP-based Dialogue Management
- URL: http://arxiv.org/abs/2009.09354v1
- Date: Sun, 20 Sep 2020 05:28:36 GMT
- Title: An Improved Approach of Intention Discovery with Machine Learning for
POMDP-based Dialogue Management
- Authors: Ruturaj Raval
- Abstract summary: Embodied Conversational Agent (ECA) works as the front end of software applications to interact with users through verbal/nonverbal expressions.
This thesis highlights the main topics related to the construction of ECA, including different approaches of dialogue management.
It proposes a cohesive framework to integrate emotion-based facial animation with improved intention discovery.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: An Embodied Conversational Agent (ECA) is an intelligent agent that works as
the front end of software applications to interact with users through
verbal/nonverbal expressions and to provide online assistance without the
limits of time, location, and language. To help to improve the experience of
human-computer interaction, there is an increasing need to empower ECA with not
only the realistic look of its human counterparts but also a higher level of
intelligence. This thesis first highlights the main topics related to the
construction of ECA, including different approaches of dialogue management, and
then discusses existing techniques of trend analysis for its application in
user classification. As a further refinement and enhancement to prior work on
ECA, this thesis research proposes a cohesive framework to integrate
emotion-based facial animation with improved intention discovery. In addition,
a machine learning technique is introduced to support sentiment analysis for
the adjustment of policy design in POMDP-based dialogue management. The
proposed research work is going to improve the accuracy of intention discovery
while reducing the length of dialogues.
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