Feudal Dialogue Management with Jointly Learned Feature Extractors
Feudal Dialogue Management with Jointly Learned Feature Extractors
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DOI：
10.18653/v1/w18-5038
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发表时间：
2018-07
期刊：
影响因子：
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通讯作者：
I. Casanueva;Paweł Budzianowski;Stefan Ultes;Florian Kreyssig;Bo-Hsiang Tseng;Yen-Chen Wu;Milica Gasic
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文献类型：
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作者：
I. Casanueva;Paweł Budzianowski;Stefan Ultes;Florian Kreyssig;Bo-Hsiang Tseng;Yen-Chen Wu;Milica Gasic
强化学习(RL)是一种很有前途的对话策略优化方法，但传统的RL算法不能扩展到较大的领域。近年来，封建对话管理通过将对话管理决策分解为两个步骤，利用领域本体来抽象每个步骤中的对话状态，从而提高了对大型领域的可扩展性。然而，为了对状态空间进行抽象，前人关于FDM域的工作依赖于手工制作的特征函数。在这项工作中，我们证明了这些特征函数可以与策略模型一起学习，同时获得相似的性能，甚至在几个环境和领域中优于手工制作的特征。
Reinforcement learning (RL) is a promising dialogue policy optimisation approach, but traditional RL algorithms fail to scale to large domains. Recently, Feudal Dialogue Management (FDM), has shown to increase the scalability to large domains by decomposing the dialogue management decision into two steps, making use of the domain ontology to abstract the dialogue state in each step. In order to abstract the state space, however, previous work on FDM relies on handcrafted feature functions. In this work, we show that these feature functions can be learned jointly with the policy model while obtaining similar performance, even outperforming the handcrafted features in several environments and domains.