Constructing and Expanding Low-Resource and Underrepresented Parallel Datasets for Indonesian Local Languages
This work addresses the data scarcity problem for NLP researchers and practitioners focusing on low-resource Indonesian languages, but it is incremental as it builds on existing methods for dataset creation.
The authors tackled the problem of limited NLP resources for Indonesian local languages by constructing Bhinneka Korpus, a multilingual parallel corpus for five languages, and tested it with IBM Model 1, showing good performance indications for further development.
In Indonesia, local languages play an integral role in the culture. However, the available Indonesian language resources still fall into the category of limited data in the Natural Language Processing (NLP) field. This is become problematic when build NLP model for these languages. To address this gap, we introduce Bhinneka Korpus, a multilingual parallel corpus featuring five Indonesian local languages. Our goal is to enhance access and utilization of these resources, extending their reach within the country. We explained in a detail the dataset collection process and associated challenges. Additionally, we experimented with translation task using the IBM Model 1 due to data constraints. The result showed that the performance of each language already shows good indications for further development. Challenges such as lexical variation, smoothing effects, and cross-linguistic variability are discussed. We intend to evaluate the corpus using advanced NLP techniques for low-resource languages, paving the way for multilingual translation models.