Name
Abstract | ||
|---|---|---|
We propose a unified Implicit Dialog framework for goal-oriented, 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. |
Year | Venue | Field |
|---|---|---|
2018 | THIRTY-SECOND AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTIETH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / EIGHTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE | Dialog box,Knowledge representation and reasoning,External Data Representation,End user,Information seeking,Computer science,Artificial intelligence,Natural language processing,Conversational search |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
2 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Song Feng | 1 | 280 | 19.55 |
| R. Chulaka Gunasekara | 2 | 15 | 4.95 |
| Sunil Shashidhara | 3 | 0 | 0.34 |
| Kshitij P. Fadnis | 4 | 2 | 3.76 |
| Lazaros Polymenakos | 5 | 251 | 28.84 |