Building Dialogue POMDPs from Expert Dialogues: An end-to-end approach
Building Dialogue POMDPs from Expert Dialogues: An end-to-end approach
复制标题
从专家对话构建对话 POMDP：端到端方法
DOI：
--
复制
发表时间：
2016
期刊：
影响因子：
--
通讯作者：
B. Chaib
中科院分区：
文献类型：
--
作者：
H. Chinaei;B. Chaib
本书讨论了部分可观察马尔可夫决策过程（POMDP）框架在对话系统中的应用。它提出POMDP作为一个正式的框架来表示不确定性，同时支持自动化的政策解决。作者提出并实现了一个端到端的学习方法的对话POMDP模型组件。从零开始，他们提出了状态，过渡模型，观察模型，最后是来自无注释和嘈杂对话的奖励模型。这些共同构成了一套重要的贡献，可能会激发进一步开展大量工作。这份简明的手稿是用简单的语言写的，充满了说明性的例子，数字和表格。
This book discusses the Partially Observable Markov Decision Process (POMDP) framework applied in dialogue systems. It presents POMDP as a formal framework to represent uncertainty explicitly while supporting automated policy solving. The authors propose and implement an end-to-end learning approach for dialogue POMDP model components. Starting from scratch, they present the state, the transition model, the observation model and then finally the reward model from unannotated and noisy dialogues. These altogether form a significant set of contributions that can potentially inspire substantial further work. This concise manuscript is written in a simple language, full of illustrative examples, figures, and tables.