@inproceedings{kang-etal-2024-xfact,
title = "{XFACT} Team0331 at {P}erspective{A}rg2024: Sampling from Bounded Clusters for Diverse Relevant Argument Retrieval",
author = "Kang, Wan Ju and
Han, Jiyoung and
Jung, Jaemin and
Thorne, James",
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-1.19",
pages = "182--188",
abstract = "This paper reports on the argument mining system submitted to the ArgMining workshop 2024 for The Perspective Argument Retrieval Shared Task (Falk et al., 2024). We com- bine the strengths of a smaller Sentence BERT model and a Large Language Model: the for- mer is fine-tuned for a contrastive embedding objective and a classification objective whereas the latter is invoked to augment the query and populate the latent space with diverse relevant arguments. We conduct an ablation study on these components to find that each contributes substantially to the diversity and relevance cri- teria for the top-k retrieval of arguments from the given corpus.",
}
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<abstract>This paper reports on the argument mining system submitted to the ArgMining workshop 2024 for The Perspective Argument Retrieval Shared Task (Falk et al., 2024). We com- bine the strengths of a smaller Sentence BERT model and a Large Language Model: the for- mer is fine-tuned for a contrastive embedding objective and a classification objective whereas the latter is invoked to augment the query and populate the latent space with diverse relevant arguments. We conduct an ablation study on these components to find that each contributes substantially to the diversity and relevance cri- teria for the top-k retrieval of arguments from the given corpus.</abstract>
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%0 Conference Proceedings
%T XFACT Team0331 at PerspectiveArg2024: Sampling from Bounded Clusters for Diverse Relevant Argument Retrieval
%A Kang, Wan Ju
%A Han, Jiyoung
%A Jung, Jaemin
%A Thorne, James
%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 kang-etal-2024-xfact
%X This paper reports on the argument mining system submitted to the ArgMining workshop 2024 for The Perspective Argument Retrieval Shared Task (Falk et al., 2024). We com- bine the strengths of a smaller Sentence BERT model and a Large Language Model: the for- mer is fine-tuned for a contrastive embedding objective and a classification objective whereas the latter is invoked to augment the query and populate the latent space with diverse relevant arguments. We conduct an ablation study on these components to find that each contributes substantially to the diversity and relevance cri- teria for the top-k retrieval of arguments from the given corpus.
%U https://aclanthology.org/2024.argmining-1.19
%P 182-188
Markdown (Informal)
[XFACT Team0331 at PerspectiveArg2024: Sampling from Bounded Clusters for Diverse Relevant Argument Retrieval](https://aclanthology.org/2024.argmining-1.19) (Kang et al., ArgMining 2024)
ACL