Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
7-2022
Abstract
We present a system for document retrieval that combines direct classification with standard content-based retrieval approaches to significantly improve the relevance of the retrieved documents. Our system exploits the availability of an imperfect but sizable amount of labeled data from past queries. For domains such as technical support, the proposed approach enhances the system’s ability to retrieve documents that are otherwise ranked very low based on content alone. The system is easy to implement and can make use of existing text ranking methods, augmenting them through the novel Q2R orchestration framework. Q2R has been extensively tested and is in use at IBM.
Discipline
Programming Languages and Compilers
Research Areas
Intelligent Systems and Optimization
Areas of Excellence
Digital transformation
Publication
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track, Seattle, Washington, July 10-15
First Page
353
Last Page
361
ISBN
9781955917728
Identifier
10.18653/v1/2022.naacl-industry.39
Publisher
Association for Computational Linguistics (ACL)
City or Country
Kerrville, TX
Citation
LIM, Shiau Hong and WYNTER, Laura.
Q2R: A query-to-resolution system for natural-language queries. (2022). Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track, Seattle, Washington, July 10-15. 353-361.
Available at: https://ink.library.smu.edu.sg/sis_research/10317
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.