@inproceedings{ren-etal-2023-thorough,
title = "A Thorough Examination on Zero-shot Dense Retrieval",
author = "Ren, Ruiyang and
Qu, Yingqi and
Liu, Jing and
Zhao, Xin and
Wu, Qifei and
Ding, Yuchen and
Wu, Hua and
Wang, Haifeng and
Wen, Ji-Rong",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.1057",
doi = "10.18653/v1/2023.findings-emnlp.1057",
pages = "15783--15796",
abstract = "Recent years have witnessed the significant advance in dense retrieval (DR) based on powerful pre-trained language models (PLM). DR models have achieved excellent performance in several benchmark datasets, while they are shown to be not as competitive as traditional sparse retrieval models (e.g., BM25) in a zero-shot retrieval setting. However, in the related literature, there still lacks a detailed and comprehensive study on zero-shot retrieval. In this paper, we present the first thorough examination of the zero-shot capability of DR models. We aim to identify the key factors and analyze how they affect zero-shot retrieval performance. In particular, we discuss the effect of several key factors related to source training set, analyze the potential bias from the target dataset, and review and compare existing zero-shot DR models. Our findings provide important evidence to better understand and develop zero-shot DR models.",
}
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<abstract>Recent years have witnessed the significant advance in dense retrieval (DR) based on powerful pre-trained language models (PLM). DR models have achieved excellent performance in several benchmark datasets, while they are shown to be not as competitive as traditional sparse retrieval models (e.g., BM25) in a zero-shot retrieval setting. However, in the related literature, there still lacks a detailed and comprehensive study on zero-shot retrieval. In this paper, we present the first thorough examination of the zero-shot capability of DR models. We aim to identify the key factors and analyze how they affect zero-shot retrieval performance. In particular, we discuss the effect of several key factors related to source training set, analyze the potential bias from the target dataset, and review and compare existing zero-shot DR models. Our findings provide important evidence to better understand and develop zero-shot DR models.</abstract>
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%0 Conference Proceedings
%T A Thorough Examination on Zero-shot Dense Retrieval
%A Ren, Ruiyang
%A Qu, Yingqi
%A Liu, Jing
%A Zhao, Xin
%A Wu, Qifei
%A Ding, Yuchen
%A Wu, Hua
%A Wang, Haifeng
%A Wen, Ji-Rong
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F ren-etal-2023-thorough
%X Recent years have witnessed the significant advance in dense retrieval (DR) based on powerful pre-trained language models (PLM). DR models have achieved excellent performance in several benchmark datasets, while they are shown to be not as competitive as traditional sparse retrieval models (e.g., BM25) in a zero-shot retrieval setting. However, in the related literature, there still lacks a detailed and comprehensive study on zero-shot retrieval. In this paper, we present the first thorough examination of the zero-shot capability of DR models. We aim to identify the key factors and analyze how they affect zero-shot retrieval performance. In particular, we discuss the effect of several key factors related to source training set, analyze the potential bias from the target dataset, and review and compare existing zero-shot DR models. Our findings provide important evidence to better understand and develop zero-shot DR models.
%R 10.18653/v1/2023.findings-emnlp.1057
%U https://aclanthology.org/2023.findings-emnlp.1057
%U https://doi.org/10.18653/v1/2023.findings-emnlp.1057
%P 15783-15796
Markdown (Informal)
[A Thorough Examination on Zero-shot Dense Retrieval](https://aclanthology.org/2023.findings-emnlp.1057) (Ren et al., Findings 2023)
ACL
- Ruiyang Ren, Yingqi Qu, Jing Liu, Xin Zhao, Qifei Wu, Yuchen Ding, Hua Wu, Haifeng Wang, and Ji-Rong Wen. 2023. A Thorough Examination on Zero-shot Dense Retrieval. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 15783–15796, Singapore. Association for Computational Linguistics.