How to Encode Domain Information in Relation Classification
<|reference_start|>How to Encode Domain Information in Relation Classification: 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 > 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.<|reference_end|>
@article{bassignana2024how,
    title={How to Encode Domain Information in Relation Classification},
    author={Elisa Bassignana, Viggo Unmack Gascou, Frida N{o}hr Laustsen, Gustav
  Kristensen, Marie Haahr Petersen, Rob van der Goot, Barbara Plank},
    journal={arXiv preprint arXiv:2404.13760},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.13760},
    primaryClass={cs.CL}
}