@inproceedings{bassignana-etal-2024-encode,
    title = "How to Encode Domain Information in Relation Classification",
    author = "Bassignana, Elisa  and
      Gascou, Viggo Unmack  and
      Laustsen, Frida N{\o}hr  and
      Kristensen, Gustav  and
      Petersen, Marie Haahr  and
      van der Goot, Rob  and
      Plank, Barbara",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.728",
    pages = "8301--8306",
    abstract = "Current language models require a lot of training data to obtain high performance. For Relation Classification (RC), many datasets are domain-specific, so combining datasets to obtain better performance is non-trivial. We explore a multi-domain training setup for RC, and attempt to improve performance by encoding domain information. Our proposed models improve {\textgreater} 2 Macro-F1 against the baseline setup, and our analysis reveals that not all the labels benefit the same: The classes which occupy a similar space across domains (i.e., their interpretation is close across them, for example {``}physical{''}) benefit the least, while domain-dependent relations (e.g., {``}part-of{''}) improve the most when encoding domain information.",
}

Current language models require a lot of training data to obtain high performance. For Relation Classification (RC), many datasets are domain-specific, so combining datasets to obtain better performance is non-trivial. We explore a multi-domain training setup for RC, and attempt to improve performance by encoding domain information. Our proposed models improve {\textgreater} 2 Macro-F1 against the baseline setup, and our analysis reveals that not all the labels benefit the same: The classes which occupy a similar space across domains (i.e., their interpretation is close across them, for example {``}physical{''}) benefit the least, while domain-dependent relations (e.g., {``}part-of{''}) improve the most when encoding domain information.
Bassignana, Elisa Gascou, Viggo Unmack Laustsen, Frida N{\o}hr Kristensen, Gustav Petersen, Marie Haahr van der Goot, Rob Plank, Barbara
How to Encode Domain Information in Relation Classification