Crowd Knowledge Enhanced Multimodal Conversational Assistant in Travel Domain
Lizi Liao Lyndon Kennedy Lynn Wilcox Tat-Seng Chua
Lyndon Kennedy
Lynn Wilcox
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Abstract
We present a new solution towards building a crowd knowledge enhanced multimodal conversational system for travel. It aims to assist users in completing various travel-related tasks, such as searching for restaurants or things to do, in a multimodal conversation manner involving both text and images. In order to achieve this goal, we ground this research on the combination of multimodal understanding and recommendation techniques which explores the possibility of a more convenient information seeking paradigm. Specifically, we build the system in a modular manner where each modular construction is enriched with crowd knowledge from social sites. To the best of our knowledge, this is the first work that attempts to build intelligent multimodal conversational systems for travel, and moves an important step towards developing human-like assistants for completion of daily life tasks. Several current challenges are also pointed out as our future directions. © 2020, Springer Nature Switzerland AG.
Keywords
Multimodal assistant, Conversational systems, Travel
Source Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher
Springer
Series/Report No.
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Date
2020-01-05
DOI
10.1007/978-3-030-37731-1_33
Type
Conference Paper