@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",
    doi = "10.18653/v1/2024.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.",
}