@inproceedings{zhang-braun-2024-twente,
    title = "Twente-{BMS}-{NLP} at {P}erspective{A}rg 2024: Combining Bi-Encoder and Cross-Encoder for Argument Retrieval",
    author = "Zhang, Leixin  and
      Braun, Daniel",
    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-1.17/",
    doi = "10.18653/v1/2024.argmining-1.17",
    pages = "164--168",
    abstract = "The paper describes our system for the Perspective Argument Retrieval Shared Task. The shared task consists of three scenarios in which relevant political arguments have to be retrieved based on queries (Scenario 1). In Scenario 2 explicit socio-cultural properties are provided and in Scenario 3 implicit socio-cultural properties within the arguments have to be used. We combined a Bi-Encoder and a Cross-Encoder to retrieve relevant arguments for each query. For the third scenario, we extracted linguistic features to predict socio-demographic labels as a separate task. However, the socio-demographic match task proved challenging due to the constraints of argument lengths and genres. The described system won both tracks of the shared task."
}