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.

通过探索基于问题回答和模板填充的技术，研究了本文中的本体转移方法对事件参数提取的有效性，以及小型模型训练在适当的源本体上可以实现零射击性能优于 GPT-3.5 或 GPT-4 的挑战。

小模型仍然是有效的跨领域论据提取器

Small Models Are (Still) Effective Cross-Domain Argument Extractors

Triplet extraction aims to extract entities and their corresponding relations
in unstructured text. Most existing methods train an extraction model on
high-quality training data, and hence are incapable of extracting relations
that were not observed during training. Generalizing the model to unseen
relations typically requires fine-tuning on synthetic training data which is
often noisy and unreliable. In this paper, we argue that reducing triplet
extraction to a template filling task over a pre-trained language model can
equip the model with zero-shot learning capabilities and enable it to leverage
the implicit knowledge in the language model. Embodying these ideas, we propose
a novel framework, ZETT (ZEro-shot Triplet extraction by Template infilling),
that is based on end-to-end generative transformers. Our experiments show that
without any data augmentation or pipeline systems, ZETT can outperform previous
state-of-the-art models with 25% less parameters. We further show that ZETT is
more robust in detecting entities and can be incorporated with automatically
generated templates for relations.

本文提出一种新的框架 ZETT，基于预训练语言模型，并采用端到端生成式 Transformer 技术，将三元组抽取任务简化为模板填充任务从而具备了零 - shot 学习能力，并能利用语言模型中的隐含知识，实验表明该方法可以在不进行数据增强和流水线系统的情况下超过之前的最优模型，对实体的检测也更为稳健。