Benchmarking Procedural Language Understanding for Low-Resource Languages: A Case Study on Turkish

TL;DR

This paper creates a Turkish procedural language understanding benchmark by expanding tutorials, defining downstream tasks, and evaluating models, highlighting the superiority of language-specific models over multilingual ones.

Contribution

It introduces a large Turkish procedural language dataset, defines multiple downstream tasks, and benchmarks various models, filling a resource gap for low-resource languages.

Findings

Language-specific models outperform multilingual models on Turkish PLU tasks.

Expanded Turkish tutorials from 2,000 to 52,000 using automated translation.

Benchmark and baseline models are publicly released.

Abstract

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.