An Improved Approach of Intention Discovery with Machine Learning for POMDP-based Dialogue Management

TL;DR

This paper proposes a machine learning-enhanced framework integrating emotion-based facial animation and sentiment analysis to improve intention discovery and dialogue efficiency in POMDP-based dialogue management for Embodied Conversational Agents.

Contribution

It introduces a novel cohesive framework combining emotion-driven facial animation with sentiment analysis to enhance intention detection in dialogue systems.

Findings

Improved accuracy of intention discovery.

Reduced dialogue length.

Enhanced user interaction experience.

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.