A Unified Implicit Dialog Framework for Conversational Search
This work addresses the challenge of developing goal-oriented conversational search applications for end-users, though it appears incremental as it builds on existing pipeline and representation methods.
The authors tackled the problem of building conversational search systems by proposing a unified Implicit Dialog framework that uses a centralized knowledge representation to ground dialog modules, enabling development across multiple domains without explicit rules. They demonstrated this by creating conversational agents for several independent domains.
We propose a unified Implicit Dialog framework for goal-oriented, information seeking tasks of Conversational Search applications. It aims to enable dialog interactions with domain data without replying on explicitly encoded the rules but utilizing the underlying data representation to build the components required for dialog interaction, which we refer as Implicit Dialog in this work. The proposed framework consists of a pipeline of End-to-End trainable modules. A centralized knowledge representation is used to semantically ground multiple dialog modules. An associated set of tools are integrated with the framework to gather end users' input for continuous improvement of the system. The goal is to facilitate 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.