@inproceedings{sidiropoulos-etal-2021-combining,
title = "Combining Lexical and Dense Retrieval for Computationally Efficient Multi-hop Question Answering",
author = "Sidiropoulos, Georgios and
Voskarides, Nikos and
Vakulenko, Svitlana and
Kanoulas, Evangelos",
editor = "Moosavi, Nafise Sadat and
Gurevych, Iryna and
Fan, Angela and
Wolf, Thomas and
Hou, Yufang and
Marasovi{\'c}, Ana and
Ravi, Sujith",
booktitle = "Proceedings of the Second Workshop on Simple and Efficient Natural Language Processing",
month = nov,
year = "2021",
address = "Virtual",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.sustainlp-1.7",
doi = "10.18653/v1/2021.sustainlp-1.7",
pages = "58--63",
abstract = "In simple open-domain question answering (QA), dense retrieval has become one of the standard approaches for retrieving the relevant passages to infer an answer. Recently, dense retrieval also achieved state-of-the-art results in multi-hop QA, where aggregating information from multiple pieces of information and reasoning over them is required. Despite their success, dense retrieval methods are computationally intensive, requiring multiple GPUs to train. In this work, we introduce a hybrid (lexical and dense) retrieval approach that is highly competitive with the state-of-the-art dense retrieval models, while requiring substantially less computational resources. Additionally, we provide an in-depth evaluation of dense retrieval methods on limited computational resource settings, something that is missing from the current literature.",
}
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<abstract>In simple open-domain question answering (QA), dense retrieval has become one of the standard approaches for retrieving the relevant passages to infer an answer. Recently, dense retrieval also achieved state-of-the-art results in multi-hop QA, where aggregating information from multiple pieces of information and reasoning over them is required. Despite their success, dense retrieval methods are computationally intensive, requiring multiple GPUs to train. In this work, we introduce a hybrid (lexical and dense) retrieval approach that is highly competitive with the state-of-the-art dense retrieval models, while requiring substantially less computational resources. Additionally, we provide an in-depth evaluation of dense retrieval methods on limited computational resource settings, something that is missing from the current literature.</abstract>
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%0 Conference Proceedings
%T Combining Lexical and Dense Retrieval for Computationally Efficient Multi-hop Question Answering
%A Sidiropoulos, Georgios
%A Voskarides, Nikos
%A Vakulenko, Svitlana
%A Kanoulas, Evangelos
%Y Moosavi, Nafise Sadat
%Y Gurevych, Iryna
%Y Fan, Angela
%Y Wolf, Thomas
%Y Hou, Yufang
%Y Marasović, Ana
%Y Ravi, Sujith
%S Proceedings of the Second Workshop on Simple and Efficient Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Virtual
%F sidiropoulos-etal-2021-combining
%X In simple open-domain question answering (QA), dense retrieval has become one of the standard approaches for retrieving the relevant passages to infer an answer. Recently, dense retrieval also achieved state-of-the-art results in multi-hop QA, where aggregating information from multiple pieces of information and reasoning over them is required. Despite their success, dense retrieval methods are computationally intensive, requiring multiple GPUs to train. In this work, we introduce a hybrid (lexical and dense) retrieval approach that is highly competitive with the state-of-the-art dense retrieval models, while requiring substantially less computational resources. Additionally, we provide an in-depth evaluation of dense retrieval methods on limited computational resource settings, something that is missing from the current literature.
%R 10.18653/v1/2021.sustainlp-1.7
%U https://aclanthology.org/2021.sustainlp-1.7
%U https://doi.org/10.18653/v1/2021.sustainlp-1.7
%P 58-63
Markdown (Informal)
[Combining Lexical and Dense Retrieval for Computationally Efficient Multi-hop Question Answering](https://aclanthology.org/2021.sustainlp-1.7) (Sidiropoulos et al., sustainlp 2021)
ACL