Pseudo-Relevance for Enhancing Document Representation
Jihyuk Kim, Seung-won Hwang, Seoho Song, Hyeseon Ko, Young-In Song
Abstract
This paper studies how to enhance the document representation for the bi-encoder approach in dense document retrieval. The bi-encoder, separately encoding a query and a document as a single vector, is favored for high efficiency in large-scale information retrieval, compared to more effective but complex architectures. To combine the strength of the two, the multi-vector representation of documents for bi-encoder, such as ColBERT preserving all token embeddings, has been widely adopted. Our contribution is to reduce the size of the multi-vector representation, without compromising the effectiveness, supervised by query logs. Our proposed solution decreases the latency and the memory footprint, up to 8- and 3-fold, validated on MSMARCO and real-world search query logs.
BibTeX
@inproceedings{kim-etal-2022-pseudo,
title = "Pseudo-Relevance for Enhancing Document Representation",
author = "Kim, Jihyuk and
Hwang, Seung-won and
Song, Seoho and
Ko, Hyeseon and
Song, Young-In",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.800/",
doi = "10.18653/v1/2022.emnlp-main.800",
pages = "11639--11652"
}