Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

3-2023

Abstract

Conversational agents, or commonly known as dialogue systems, have gained escalating popularity in recent years. Their widespread applications support conversational interactions with users and accomplishing various tasks as personal assistants. However, one key weakness in existing conversational agents is that they only learn to passively answer user queries via training on pre-collected and manually-labeled data. Such passiveness makes the interaction modeling and system-building process relatively easier, but it largely hinders the possibility of being human-like hence lowering the user engagement level. In this tutorial, we introduce and discuss methods to equip conversational agents with the ability to interact with end users in a more proactive way. This three-hour tutorial is divided into three parts and includes two interactive exercises. It reviews and presents recent advancements on the topic, focusing on automatically expanding ontology space, actively driving conversation by asking questions or strategically shifting topics, and retrospectively conducting response quality control.

Keywords

conversational AI, conversational search, proactive conversational agents, task-oriented dialogue systems

Discipline

Artificial Intelligence and Robotics

Research Areas

Intelligent Systems and Optimization

Publication

WSDM 2023: Proceedings of the 16th ACM International Conference on Web Search and Data Mining: Singapore, February 27-March 3

First Page

1244

Last Page

1247

ISBN

9781450394079

Identifier

10.1145/3539597.3572724

Publisher

ACM

City or Country

New York

Copyright Owner and License

Authors

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Additional URL

https://doi.org/10.1145/3539597.3572724

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