Design Challenges for a Multi-Perspective Search Engine

Sihao Chen, Siyi Liu, Xander Uyttendaele, Yi Zhang, William Bruno, Dan Roth


Abstract
Many users turn to document retrieval systems (e.g. search engines) to seek answers to controversial or open-ended questions. However, classical document retrieval systems fall short at delivering users a set of direct and diverse responses in such cases, which requires identifying responses within web documents in the context of the query, and aggregating the responses based on their different perspectives. The goal of this work is to survey and study the user information needs for building a multi-perspective search engine of such. We examine the challenges of synthesizing such language understanding objectives with document retrieval, and study a new perspective-oriented document retrieval paradigm. We discuss and assess the inherent natural language understanding challenges one needs to address in order to achieve the goal. Following the design challenges and principles, we propose and evaluate a practical prototype pipeline system. We use the prototype system to conduct a user survey in order to assess the utility of our paradigm, as well as understanding the user information needs when issuing controversial and open-ended queries to a search engine.
Anthology ID:
2022.findings-naacl.22
Volume:
Findings of the Association for Computational Linguistics: NAACL 2022
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
293–303
Language:
URL:
https://aclanthology.org/2022.findings-naacl.22
DOI:
10.18653/v1/2022.findings-naacl.22
Bibkey:
Cite (ACL):
Sihao Chen, Siyi Liu, Xander Uyttendaele, Yi Zhang, William Bruno, and Dan Roth. 2022. Design Challenges for a Multi-Perspective Search Engine. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 293–303, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
Design Challenges for a Multi-Perspective Search Engine (Chen et al., Findings 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.findings-naacl.22.pdf
Video:
 https://aclanthology.org/2022.findings-naacl.22.mp4
Code
 cogcomp/multi-persp-search-engine