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

arXiv: https://arxiv.org/abs/2302.09611 · HTML: https://mlsanity.com/paper/2302.09611/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 31 references that are in the map.
- First posted on arXiv 19 Feb 2023.

## Builds on (most cited first, 10 of 10)

- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [ParsBERT: Transformer-based Model for Persian Language Understanding](https://mlsanity.com/paper/2005.12515/) (2020)
- [Entity Projection via Machine Translation for Cross-Lingual NER](https://mlsanity.com/paper/1909.05356/) (2019)
- [MultiCoNER: A Large-scale Multilingual dataset for Complex Named Entity Recognition](https://mlsanity.com/paper/2208.14536/) (2022)
- [SMaLL-100: Introducing Shallow Multilingual Machine Translation Model for Low-Resource Languages](https://mlsanity.com/paper/2210.11621/) (2022)
- [NSURL-2019 Task 7: Named Entity Recognition (NER) in Farsi](https://mlsanity.com/paper/2003.09029/) (2020)
- [An Evaluation of Persian-English Machine Translation Datasets with Transformers](https://mlsanity.com/paper/2302.00321/) (2023)

## Built on by (most cited first, 1 of 1)

- [Transformer-based Named Entity Recognition in Construction Supply Chain Risk Management in Australia](https://mlsanity.com/paper/2311.13755/) (2023)

## Abstract (arXiv)

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.