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.
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Datasets
WikiEvents
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Methods
Absolute Position Encodings •
Adam •
Attention Dropout •
BPE •
Cosine Annealing •
Dense Connections •
Dropout •
Fixed Factorized Attention •
GELU •
GPT-3 •
GPT-4 •
Label Smoothing •
Layer Normalization •
Linear Layer •
Linear Warmup With Cosine Annealing •
Multi-Head Attention •
Ontology •
Position-Wise Feed-Forward Layer •
Residual Connection •
Scaled Dot-Product Attention •
Softmax •
Strided Attention •
Transformer •
Weight Decay
