@inproceedings{bondarenko-etal-2024-deepct,
title = "{D}eep{CT}-enhanced Lexical Argument Retrieval",
author = {Bondarenko, Alexander and
Fr{\"o}be, Maik and
Hollatz, Danik and
Merker, Jan and
Hagen, Matthias},
editor = "Ajjour, Yamen and
Bar-Haim, Roy and
El Baff, Roxanne and
Liu, Zhexiong and
Skitalinskaya, Gabriella",
booktitle = "Proceedings of the 11th Workshop on Argument Mining (ArgMining 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.argmining-1.3",
pages = "29--35",
abstract = "The recent Touch{\'e} lab{'}s argument retrieval task focuses on controversial topics like {`}Should bottled water be banned?{'} and asks to retrieve relevant pro/con arguments. Interestingly, the most effective systems submitted to that task still are based on lexical retrieval models like BM25. In other domains, neural retrievers that capture semantics are more effective than lexical baselines. To add more {``}semantics{''} to argument retrieval, we propose to combine lexical models with DeepCT-based document term weights. Our evaluation shows that our approach is more effective than all the systems submitted to the Touch{\'e} lab while being on par with modern neural re-rankers that themselves are computationally more expensive.",
}
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<abstract>The recent Touché lab’s argument retrieval task focuses on controversial topics like ‘Should bottled water be banned?’ and asks to retrieve relevant pro/con arguments. Interestingly, the most effective systems submitted to that task still are based on lexical retrieval models like BM25. In other domains, neural retrievers that capture semantics are more effective than lexical baselines. To add more “semantics” to argument retrieval, we propose to combine lexical models with DeepCT-based document term weights. Our evaluation shows that our approach is more effective than all the systems submitted to the Touché lab while being on par with modern neural re-rankers that themselves are computationally more expensive.</abstract>
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<url>https://aclanthology.org/2024.argmining-1.3</url>
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%0 Conference Proceedings
%T DeepCT-enhanced Lexical Argument Retrieval
%A Bondarenko, Alexander
%A Fröbe, Maik
%A Hollatz, Danik
%A Merker, Jan
%A Hagen, Matthias
%Y Ajjour, Yamen
%Y Bar-Haim, Roy
%Y El Baff, Roxanne
%Y Liu, Zhexiong
%Y Skitalinskaya, Gabriella
%S Proceedings of the 11th Workshop on Argument Mining (ArgMining 2024)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F bondarenko-etal-2024-deepct
%X The recent Touché lab’s argument retrieval task focuses on controversial topics like ‘Should bottled water be banned?’ and asks to retrieve relevant pro/con arguments. Interestingly, the most effective systems submitted to that task still are based on lexical retrieval models like BM25. In other domains, neural retrievers that capture semantics are more effective than lexical baselines. To add more “semantics” to argument retrieval, we propose to combine lexical models with DeepCT-based document term weights. Our evaluation shows that our approach is more effective than all the systems submitted to the Touché lab while being on par with modern neural re-rankers that themselves are computationally more expensive.
%U https://aclanthology.org/2024.argmining-1.3
%P 29-35
Markdown (Informal)
[DeepCT-enhanced Lexical Argument Retrieval](https://aclanthology.org/2024.argmining-1.3) (Bondarenko et al., ArgMining 2024)
ACL
- Alexander Bondarenko, Maik Fröbe, Danik Hollatz, Jan Merker, and Matthias Hagen. 2024. DeepCT-enhanced Lexical Argument Retrieval. In Proceedings of the 11th Workshop on Argument Mining (ArgMining 2024), pages 29–35, Bangkok, Thailand. Association for Computational Linguistics.