Exploring the Potential of Machine Translation for Generating Named Entity Datasets: A Case Study between Persian and English

TL;DR

This paper investigates using machine translation to generate Persian named entity datasets from English data, demonstrating promising results and potential for improving low-resource language NER systems.

Contribution

It introduces a novel approach of leveraging machine translation to create Persian NER datasets and evaluates their effectiveness with transformer models.

Findings

Highest F1 score of 85.11% on CoNLL 2003 dataset

Lower F1 score of 40.02% on WNUT 2017 dataset

Machine translation can augment datasets for low-resource languages

Abstract

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.