论文标题
基于POMDP的对话管理的机器学习的意图发现的改进方法
An Improved Approach of Intention Discovery with Machine Learning for POMDP-based Dialogue Management
论文作者
论文摘要
体现的对话代理（ECA）是一种智能代理，它可以用作软件应用程序的前端，可以通过口头/非语言表达式与用户进行交互，并在没有时间，位置和语言的限制的情况下提供在线帮助。为了帮助改善人类计算机互动的经验，不仅要赋予ECA的现实外观，而且还具有更高的智能水平。该论文首先强调了与ECA的构建有关的主要主题，包括对话管理的不同方法，然后讨论了其在用户分类中的应用趋势分析技术。作为对ECA先前工作的进一步完善和增强，本文研究提出了一个有凝聚力的框架，以将基于情感的面部动画与改善意图发现相结合。此外，引入了机器学习技术，以支持情感分析，以调整基于POMDP的对话管理中的策略设计。拟议的研究工作将提高意图发现的准确性，同时减少对话的时间长度。
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.
