# Automatic Romanization of Arabic Bibliographic Records

arXiv: https://arxiv.org/abs/2103.07199 · HTML: https://mlsanity.com/paper/2103.07199/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 20 references that are in the map.
- First posted on arXiv 12 Mar 2021.

## Builds on (most cited first, 3 of 3)

- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [Arabizi Detection and Conversion to Arabic](https://mlsanity.com/paper/1306.6755/) (2013)

## Built on by (most cited first, 3 of 3)

- [Arabic Diacritics in the Wild: Exploiting Opportunities for Improved Diacritization](https://mlsanity.com/paper/2406.05760/) (2024)
- [Proper Noun Diacritization for Arabic Wikipedia: A Benchmark Dataset](https://mlsanity.com/paper/2505.02656/) (2025)
- [Data Augmentation for Maltese NLP using Transliterated and Machine Translated Arabic Data](https://mlsanity.com/paper/2509.12853/) (2025)

## Abstract (arXiv)

International library standards require cataloguers to tediously input Romanization of their catalogue records for the benefit of library users without specific language expertise. In this paper, we present the first reported results on the task of automatic Romanization of undiacritized Arabic bibliographic entries. This complex task requires the modeling of Arabic phonology, morphology, and even semantics. We collected a 2.5M word corpus of parallel Arabic and Romanized bibliographic entries, and benchmarked a number of models that vary in terms of complexity and resource dependence. Our best system reaches 89.3% exact word Romanization on a blind test set. We make our data and code publicly available.