Event argument extraction has long been studied as a sequential prediction
problem with extractive-based methods, tackling each argument in isolation.
Although recent work proposes generation-based methods to capture
cross-argument dependency, they require generating and post-processing a
complicated target sequence (template). Motivated by these observations and
recent pretrained language models' capabilities of learning from
demonstrations. We propose a retrieval-augmented generative QA model (R-GQA)
for event argument extraction. It retrieves the most similar QA pair and
augments it as prompt to the current example's context, then decodes the
arguments as answers. Our approach outperforms substantially prior methods
across various settings (i.e. fully supervised, domain transfer, and fewshot
learning). Finally, we propose a clustering-based sampling strategy (JointEnc)
and conduct a thorough analysis of how different strategies influence the
few-shot learning performance. The implementations are available at https://
this http URL

本文提出了一种论据提取的检索增强生成问答模型，结合了生成方法和检索技术，从而提高了事件论据提取的性能，并提出了一种基于聚类的样本采样策略，以进一步提高模型在 few-shot 学习中的表现。

检索辅助生成式问答用于事件论元抽取

Retrieval-Augmented Generative Question Answering for Event Argument Extraction

Zero-shot transfer learning for dialogue state tracking (DST) enables us to
handle a variety of task-oriented dialogue domains without the expense of
collecting in-domain data. In this work, we propose to transfer the
\textit{cross-task} knowledge from general question answering (QA) corpora for
the zero-shot DST task. Specifically, we propose TransferQA, a transferable
generative QA model that seamlessly combines extractive QA and multi-choice QA
via a text-to-text transformer framework, and tracks both categorical slots and
non-categorical slots in DST. In addition, we introduce two effective ways to
construct unanswerable questions, namely, negative question sampling and
context truncation, which enable our model to handle "none" value slots in the
zero-shot DST setting. The extensive experiments show that our approaches
substantially improve the existing zero-shot and few-shot results on MultiWoz.
Moreover, compared to the fully trained baseline on the Schema-Guided Dialogue
dataset, our approach shows better generalization ability in unseen domains.

本论文提出了 TransferQA，一个可转移的生成式问答模型，它通过文本到文本转换框架无缝地结合了抽取式问答和多选式问答，有效地处理了零 - shot DST 任务中的分类和非分类插槽，并介绍了两种构建无法回答的问题的有效方法，即负面问题抽样和上下文截断。经过实验证明，在 MultiWoz 上，我们的方法显著改善了现有的零 - shot 和少 - shot 结果，与 Schema-Guided Dialogue 数据集上的全面训练基线相比，我们的方法在未知领域中表现出更好的泛化能力。