Small Models Are (Still) Effective Cross-Domain Argument Extractors
William Gantt Aaron Steven White
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.