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User-aware dialogue management policies over attributed bi-automata

机译:基于双自动机的用户感知对话管理策略

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Designing dialogue policies that take user behavior into account is complicated due to user variability and behavioral uncertainty. Attributed probabilistic finite-state bi-automata (A-PFSBA) have proven to be a promising framework to develop dialogue managers that capture the users' actions in its structure and adapt to them online, yet developing policies robust to high user uncertainty is still challenging. In this paper, the theoretical A-PFSBA dialogue management framework is augmented by formally defining the notation of exploitation policies over its structure. Under such definition, multiple path-based policies are implemented, those that take into account external information and those which do not. These policies are evaluated on the Let's Go corpus, before and after an online learning process whose goal is to update the initial model through the interaction with end users. In these experiments the impact of user uncertainty and the model structural learning is thoroughly analyzed.
机译:由于用户的可变性和行为的不确定性,设计考虑用户行为的对话策略非常复杂。事实证明,归因概率有限状态双自动机(A-PFSBA)是开发对话管理器的有前途的框架,这些对话管理器可以捕获用户结构中的动作并在线适应它们,但是制定对用户高度不确定性稳健的策略仍然具有挑战性。在本文中,通过在结构上正式定义剥削政策的概念,从而扩充了理论上的A-PFSBA对话管理框架。在这样的定义下,实施了多个基于路径的策略,那些策略考虑了外部信息,而那些策略没有考虑。在在线学习过程之前和之后,这些策略在Let's Go语料库中进行评估,其目的是通过与最终用户的交互来更新初始模型。在这些实验中,彻底分析了用户不确定性和模型结构学习的影响。

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