@inproceedings{xiao-etal-2024-jina,
title = "{J}ina-{C}ol{BERT}-v2: A General-Purpose Multilingual Late Interaction Retriever",
author = {Jha, Rohan and
Wang, Bo and
G{\"u}nther, Michael and
Mastrapas, Georgios and
Sturua, Saba and
Mohr, Isabelle and
Koukounas, Andreas and
Wang, Mohammad Kalim and
Wang, Nan and
Xiao, Han},
editor = {S{\"a}lev{\"a}, Jonne and
Owodunni, Abraham},
booktitle = "Proceedings of the Fourth Workshop on Multilingual Representation Learning (MRL 2024)",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.mrl-1.11/",
doi = "10.18653/v1/2024.mrl-1.11",
pages = "159--166",
abstract = "Multi-vector dense models, such as ColBERT, have proven highly effective in information retrieval. ColBERT`s late interaction scoring approximates the joint query-document attention seen in cross-encoders while maintaining inference efficiency closer to traditional dense retrieval models, thanks to its bi-encoder architecture and recent optimizations in indexing and search. In this paper, we introduce a novel architecture and a training framework to support long context window and multilingual retrieval. Leveraging Matryoshka Representation Loss, we further demonstrate that the reducing the embedding dimensionality from 128 to 64 has insignificant impact on the model`s retrieval performance and cut storage requirements by up to 50{\%}. Our new model, Jina-ColBERT-v2, demonstrates strong performance across a range of English and multilingual retrieval tasks,"
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%0 Conference Proceedings
%T Jina-ColBERT-v2: A General-Purpose Multilingual Late Interaction Retriever
%A Jha, Rohan
%A Wang, Bo
%A Günther, Michael
%A Mastrapas, Georgios
%A Sturua, Saba
%A Mohr, Isabelle
%A Koukounas, Andreas
%A Wang, Mohammad Kalim
%A Wang, Nan
%A Xiao, Han
%Y Sälevä, Jonne
%Y Owodunni, Abraham
%S Proceedings of the Fourth Workshop on Multilingual Representation Learning (MRL 2024)
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F xiao-etal-2024-jina
%X Multi-vector dense models, such as ColBERT, have proven highly effective in information retrieval. ColBERT‘s late interaction scoring approximates the joint query-document attention seen in cross-encoders while maintaining inference efficiency closer to traditional dense retrieval models, thanks to its bi-encoder architecture and recent optimizations in indexing and search. In this paper, we introduce a novel architecture and a training framework to support long context window and multilingual retrieval. Leveraging Matryoshka Representation Loss, we further demonstrate that the reducing the embedding dimensionality from 128 to 64 has insignificant impact on the model‘s retrieval performance and cut storage requirements by up to 50%. Our new model, Jina-ColBERT-v2, demonstrates strong performance across a range of English and multilingual retrieval tasks,
%R 10.18653/v1/2024.mrl-1.11
%U https://aclanthology.org/2024.mrl-1.11/
%U https://doi.org/10.18653/v1/2024.mrl-1.11
%P 159-166
Markdown (Informal)
[Jina-ColBERT-v2: A General-Purpose Multilingual Late Interaction Retriever](https://aclanthology.org/2024.mrl-1.11/) (Jha et al., MRL 2024)
ACL
- Rohan Jha, Bo Wang, Michael Günther, Georgios Mastrapas, Saba Sturua, Isabelle Mohr, Andreas Koukounas, Mohammad Kalim Wang, Nan Wang, and Han Xiao. 2024. Jina-ColBERT-v2: A General-Purpose Multilingual Late Interaction Retriever. In Proceedings of the Fourth Workshop on Multilingual Representation Learning (MRL 2024), pages 159–166, Miami, Florida, USA. Association for Computational Linguistics.