@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://preview.aclanthology.org/jlcl-multiple-ingestion/2024.argmining-1.19/",
    doi = "10.18653/v1/2024.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."
}