Embark on DenseQuest: A System for Selecting the Best Dense Retriever for a Custom Collection.
Ekaterina Khramtsova|Teerapong Leelanupab|Shengyao Zhuang|Mahsa Baktashmotlagh|Guido Zuccon
| Anthology ID: | DBLP:conf/sigir/KhramtsovaLZBZ24 |
|---|---|
| Volume: | Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2024, Washington DC, USA, July 14-18, 2024 |
| Year: | 2024 |
| Venue: | Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) |
| Publisher: | ACM |
| Pages: | 2739-2743 |
| URL: | https://doi.org/10.1145/3626772.3657674 |
| DOI: | https://doi.org/10.1145/3626772.3657674 |
| DBLP: | conf/sigir/KhramtsovaLZBZ24 |
| BibTeX: |
@inproceedings{khramtsova-2024-embark,
author = {Ekaterina Khramtsova and
Teerapong Leelanupab and
Shengyao Zhuang and
Mahsa Baktashmotlagh and
Guido Zuccon},
editor = {Grace Hui Yang and
Hongning Wang and
Sam Han and
Claudia Hauff and
Guido Zuccon and
Yi Zhang},
title = {{Embark on DenseQuest: A System for Selecting the Best Dense Retriever for a Custom Collection}},
booktitle = {{Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2024, Washington DC, USA, July 14-18, 2024}},
pages = {2739--2743},
publisher = {ACM},
year = {2024},
url = {https://doi.org/10.1145/3626772.3657674},
doi = {https://doi.org/10.1145/3626772.3657674}
}