A Unified Implicit Dialog Framework for Conversational Commerce

We propose a unified Implicit Dialog framework for goaloriented, information seeking tasks of Conversational Commerce applications. It aims to enable the dialog interactions with domain data without replying on the explicitly encoded rules but utilizing the underlying data representation to build the components required for the interactions, which we refer as Implicit Dialog in this work. The proposed framework consists of a pipeline of End-to-End trainable modules. It generates a centralized knowledge representation to semantically ground multiple sub-modules. The framework is also integrated with an associated set of tools to gather end users’ input for continuous improvement of the system. This framework is designed to facilitate fast development of conversational systems by identifying the components and the data that can be adapted and reused across many end-user applications. We demonstrate our approach by creating conversational agents for several independent domains.

Authors

• Song Feng AWS AI Labs
• R. Chulaka Gunasekara
• Sunil Shashidhara
• Kshitij Fadnis
• Lazaros Polymenakos

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