Exploring the Potential of Machine Translation for Generating Named Entity Datasets: A Case Study between Persian and English
This study focuses on the generation of Persian named entity datasets through
the application of machine translation on English datasets. The generated
datasets were evaluated by experimenting with one monolingual and one
multilingual transformer model. Notably, the CoNLL 2003 dataset has achieved
the highest F1 score of 85.11%. In contrast, the WNUT 2017 dataset yielded the
lowest F1 score of 40.02%. The results of this study highlight the potential of
machine translation in creating high-quality named entity recognition datasets
for low-resource languages like Persian. The study compares the performance of
these generated datasets with English named entity recognition systems and
provides insights into the effectiveness of machine translation for this task.
Additionally, this approach could be used to augment data in low-resource
language or create noisy data to make named entity systems more robust and
improve them.