Understanding procedural natural language (e.g., step-by-step instructions) is a crucial step to execution and planning. However, while there are ample corpora and downstream tasks available in English, the field lacks such resources for most languages. To address this gap, we conduct a case study on Turkish procedural texts. We first expand the number of tutorials in Turkish wikiHow from 2,000 to 52,000 using automated translation tools, where the translation quality and loyalty to the original meaning are validated by a team of experts on a random set. Then, we generate several downstream tasks on the corpus, such as linking actions, goal inference, and summarization. To tackle these tasks, we implement strong baseline models via fine-tuning large language-specific models such as TR-BART and BERTurk, as well as multilingual models such as mBART, mT5, and XLM. We find that language-specific models consistently outperform their multilingual models by a significant margin across most procedural language understanding (PLU) tasks. We release our corpus, downstream tasks and the baseline models with https://github.com/ GGLAB-KU/turkish-plu.

翻译：理解程序性自然语言（例如，逐步指令）是执行与规划的关键步骤。然而，尽管英语中已有丰富的语料库和下游任务，但大多数语言在此领域仍缺乏类似资源。为填补这一空白，我们针对土耳其语程序性文本开展了案例研究。我们首先利用自动翻译工具将土耳其语wikiHow中的教程数量从2,000篇扩展至52,000篇，并通过专家团队对随机样本进行验证，确保翻译质量及对原意的忠实度。随后，我们基于该语料库生成了若干下游任务，如动作链接、目标推断和摘要生成。为处理这些任务，我们通过微调大型语言专用模型（如TR-BART和BERTurk）及多语言模型（如mBART、mT5和XLM）实现了强基线模型。研究发现，在多数程序性语言理解任务中，语言专用模型显著优于多语言模型。我们已在https://github.com/GGLAB-KU/turkish-plu 发布语料库、下游任务及基线模型。