Small Models Are (Still) Effective Cross-Domain Argument Extractors
<|reference_start|>Small Models Are (Still) Effective Cross-Domain Argument Extractors: Effective ontology transfer has been a major goal of recent work on event argument extraction (EAE). Two methods in particular -- question answering (QA) and template infilling (TI) -- have emerged as promising approaches to this problem. However, detailed explorations of these techniques' ability to actually enable this transfer are lacking. In this work, we provide such a study, exploring zero-shot transfer using both techniques on six major EAE datasets at both the sentence and document levels. Further, we challenge the growing reliance on LLMs for zero-shot extraction, showing that vastly smaller models trained on an appropriate source ontology can yield zero-shot performance superior to that of GPT-3.5 or GPT-4.<|reference_end|>
@article{gantt2024small,
    title={Small Models Are (Still) Effective Cross-Domain Argument Extractors},
    author={William Gantt and Aaron Steven White},
    journal={arXiv preprint arXiv:2404.08579},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.08579},
    primaryClass={cs.CL cs.AI cs.LG}
}