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.- Anthology ID:
- 2022.naacl-industry.39
- Volume:
- Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track
- Month:
- July
- Year:
- 2022
- Address:
- Hybrid: Seattle, Washington + Online
- Editors:
- Anastassia Loukina, Rashmi Gangadharaiah, Bonan Min
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 353–361
- Language:
- URL:
- https://aclanthology.org/2022.naacl-industry.39
- DOI:
- 10.18653/v1/2022.naacl-industry.39
- Cite (ACL):
- Shiau Hong Lim and Laura Wynter. 2022. Q2R: A Query-to-Resolution System for Natural-Language Queries. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track, pages 353–361, Hybrid: Seattle, Washington + Online. Association for Computational Linguistics.
- Cite (Informal):
- Q2R: A Query-to-Resolution System for Natural-Language Queries (Lim & Wynter, NAACL 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2022.naacl-industry.39.pdf
- Data
- MS MARCO