QuadrupletBERT: An Efficient Model For Embedding-Based Large-Scale Retrieval
Peiyang Liu, Sen Wang, Xi Wang, Wei Ye, Shikun Zhang
Abstract
The embedding-based large-scale query-document retrieval problem is a hot topic in the information retrieval (IR) field. Considering that pre-trained language models like BERT have achieved great success in a wide variety of NLP tasks, we present a QuadrupletBERT model for effective and efficient retrieval in this paper. Unlike most existing BERT-style retrieval models, which only focus on the ranking phase in retrieval systems, our model makes considerable improvements to the retrieval phase and leverages the distances between simple negative and hard negative instances to obtaining better embeddings. Experimental results demonstrate that our QuadrupletBERT achieves state-of-the-art results in embedding-based large-scale retrieval tasks.
BibTeX
@inproceedings{liu-etal-2021-quadrupletbert,
title = "{Q}uadruplet{BERT}: An Efficient Model For Embedding-Based Large-Scale Retrieval",
author = "Liu, Peiyang and
Wang, Sen and
Wang, Xi and
Ye, Wei and
Zhang, Shikun",
editor = "Toutanova, Kristina and
Rumshisky, Anna and
Zettlemoyer, Luke and
Hakkani-Tur, Dilek and
Beltagy, Iz and
Bethard, Steven and
Cotterell, Ryan and
Chakraborty, Tanmoy and
Zhou, Yichao",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.292/",
doi = "10.18653/v1/2021.naacl-main.292",
pages = "3734--3739"
}