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 this https URL
GGLAB-KU/turkish-plu.

通过使用自动翻译工具从 2,000 篇文章扩充到 52,000 篇，我们针对土耳其编程指南进行了个案研究，生成了包含链接动作、目标推断和总结等多个任务的关于土耳其编程指南的语料库，并实施了强大的基准模型，发现针对步骤指令文本的语言专用模型在大多数任务上始终优于多语言模型。