Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

7-2023

Abstract

Asking clarifying questions has become a key element of various conversational systems, allowing for an effective resolution of ambiguity and uncertainty through natural language questions. Despite the extensive applications of spatial information grounded dialogues, it remains an understudied area on learning to ask clarification questions with the capability of spatial reasoning. In this work, we propose a novel method, named SpatialCQ, for this problem. Specifically, we first align the representation space between textual and spatial information by encoding spatial states with textual descriptions. Then a multi-relational graph is constructed to capture the spatial relations and enable spatial reasoning with relational graph attention networks. Finally, a unified encoder is adopted to fuse the multimodal information for asking clarification questions. Experimental results on the latest IGLU dataset show the superiority of the proposed method over existing approaches.

Keywords

Asking clarification question, Conversational systems, Effective resolutions, Key elements, Natural language questions, Novel methods, Relational graph, Spatial informations, Spatial reasoning, Uncertainty

Discipline

Databases and Information Systems | Information Security

Research Areas

Data Science and Engineering; Information Systems and Management

Areas of Excellence

Digital transformation

Publication

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, Taipei, Taiwan, 2023 July 23-27

First Page

2113

Last Page

2117

ISBN

9781450394086

Identifier

10.1145/3539618.3592009

Publisher

ACM

City or Country

New York

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1145/3539618.3592009

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