巴斯克语事件抽取：类型学驱动的跨语言迁移学习分析
Event Extraction in Basque: Typologically motivated Cross-Lingual Transfer-Learning Analysis
AI总结：
本文以类型学独特的巴斯克语为目标语言，通过三个事件抽取任务和72个语言对分析，揭示源语言与目标语言在书写文字、形态特征和语序上的相似性对不同类型跨语言迁移任务的影响，并发布了巴斯克语事件抽取数据集EusIE。
摘要：
跨语言迁移学习在低资源语言的事件抽取中被广泛使用，其做法是使用一个多语言语言模型在源语言上进行训练，然后将其应用于目标语言。本文研究源语言与目标语言之间的类型学相似性是否会影响跨语言迁移的性能，这是一个尚未被充分探索的课题。我们首先以巴斯克语作为目标语言，巴斯克语是一个理想的目标语言，因为它在类型学上与周边语言存在显著差异。我们在三个事件抽取任务上的实验表明，源语言与目标语言之间共有的语言特征确实会影响迁移质量。对72个语言对的进一步分析揭示：对于涉及token分类的任务，如实体识别和事件触发词识别，共同的书写文字和形态特征能够产生更高质量的跨语言迁移；相比之下，对于涉及结构预测的任务，如论元抽取，共同的语序是最相关的特征。此外，我们表明在跨语言设置下，当增加训练数据规模时，并非所有语言都以相同的方式扩展性能。为了进行实验，我们引入了EusIE，一个面向巴斯克语的事件抽取数据集，该数据集遵循多语言事件抽取数据集（MEE）的规范。数据集和代码已公开发布。
英文摘要：
Cross-lingual transfer-learning is widely used in Event Extraction for low-resource languages and involves a Multilingual Language Model that is trained in a source language and applied to the target language. This paper studies whether the typological similarity between source and target languages impacts the performance of cross-lingual transfer, an under-explored topic. We first focus on Basque as the target language, which is an ideal target language because it is typologically different from surrounding languages. Our experiments on three Event Extraction tasks show that the shared linguistic characteristic between source and target languages does have an impact on transfer quality. Further analysis of 72 language pairs reveals that for tasks that involve token classification such as entity and event trigger identification, common writing script and morphological features produce higher quality cross-lingual transfer. In contrast, for tasks involving structural prediction like argument extraction, common word order is the most relevant feature. In addition, we show that when increasing the training size, not all the languages scale in the same way in the cross-lingual setting. To perform the experiments we introduce EusIE, an event extraction dataset for Basque, which follows the Multilingual Event Extraction dataset (MEE). The dataset and code are publicly available.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
