Effective handling of dialogue state in the hidden information state POMDP-based dialogue manager
Effective handling of dialogue state in the hidden information state POMDP-based dialogue manager
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隐藏信息状态下对话状态的有效处理 基于POMDP的对话管理器
DOI：
10.1145/1966407.1966409
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发表时间：
2011
期刊：
影响因子：
--
通讯作者：
Gašic M
中科院分区：
文献类型：
--
作者：
Gašic M
有效的对话管理在很大程度上依赖于在对话状态中编码的信息。为了将强化学习用于策略优化，必须将对话建模为马尔可夫决策过程。这要求对话状态必须对在该状态之前的对话期间获得的所有相关信息进行编码。这可以通过组合用户目标、对话历史和最后一个用户动作来实现，以形成对话状态。此外，为了获得对输入错误的稳健性，对话必须被建模为部分可观测的马尔可夫决策过程(POMDP)，因此，必须在每个对话轮次保持对所有可能状态的分布。这造成了潜在的计算限制，因为可能存在非常多的对话状态。隐藏信息状态模型提供了一种确保基于POMDP的对话模型的可控性的原则性方法。该模型的主要特点是将用户目标分组到对话期间动态构建的分区中。在本文中，我们将进一步扩展该模型以包含补充的概念。这允许表示更复杂的用户目标，并且能够实现有效的修剪技术，该技术比现有方法更有效地在有限的计算资源内保持整体系统性能。
Effective dialogue management is critically dependent on the information that is encoded in the dialogue state. In order to deploy reinforcement learning for policy optimization, dialogue must be modeled as a Markov Decision Process. This requires that the dialogue state must encode all relevent information obtained during the dialogue prior to that state. This can be achieved by combining the user goal, the dialogue history, and the last user action to form the dialogue state. In addition, to gain robustness to input errors, dialogue must be modeled as a Partially Observable Markov Decision Process (POMDP) and hence, a distribution over all possible states must be maintained at every dialogue turn. This poses a potential computational limitation since there can be a very large number of dialogue states. The Hidden Information State model provides a principled way of ensuring tractability in a POMDP-based dialogue model. The key feature of this model is the grouping of user goals into partitions that are dynamically built during the dialogue. In this article, we extend this model further to incorporate the notion of complements. This allows for a more complex user goal to be represented, and it enables an effective pruning technique to be implemented that preserves the overall system performance within a limited computational resource more effectively than existing approaches.
使用对话示例进行口语对话管理的基于框架的概率框架
DOI：
10.3115/1622064.1622088
发表时间：
2008
期刊：
2008 IEEE Spoken Language Technology Workshop
影响因子：
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作者：
Kyungduk Kim;Cheongjae Lee;Sangkeun Jung;G. G. Lee
通讯作者：
G. G. Lee
影响因子：
4.3
作者：
Thomson, Blaise;Young, Steve
通讯作者：
Young, Steve