A tool and dataset for the automatic Romanization of Arabic text in the ALA-LC Romanization standard.
Example of Arabic source and target Romanization with MARC field/subfield tag and tag description (see publication for more details).
- Fadhl Eryani & Nizar Habash. Automatic Romanization of Arabic Bibliographic Records. Proceedings of the Sixth Arabic Natural Language Processing Workshop. 2021.
http://romanize-arabic.camel-lab.com/
This demo is based on the “MLE Simple” model described in Eryani & Habash (2021), with slight modifications. This model, selected for its speed, uses a large corpus of training data to calculate the most frequent Romanization given a certain Arabic input. The system achieves 90% accuracy (ignoring capitalization errors) and 84% exact match accuracy. In out-of-vocabulary scenarios, about 3% of the words, the system falls back on a simple character transliteration technique that Romanizes the input as is, without guessing unmarked vowels or splitting proclitics.
Check out the first release.
- Fadhl Eryani
- Nizar Habash
This project was developed using python 3.6, and tested in macOS and linux environments. First you must install required packages:
pip3 install -r requirements.txt
To run the MADAMIRA morphological analyser and disambiguator, you must have a MADAMIRA distribution in the project directory, which you can obtain from here.
The database used is almor-msa-s31.db (see documentation/MADAMIRA-UserManual p6 for more info). If you have access to this database, place the database file inside MADAMIRA/resources/ and setup the MADAMIRA config file located in MADAMIRA/config/almor.properties by updating the following flag:
ALMOR.text.MSA.database.name=almor-s31.db
For info on the Buckwalter Part-of-Speech tag set used by MADAMIRA, see /documentation/ATB-POSGuidelines-v3.7.pdf
- MADAMIRA will not run on versions of Java above 9
- We used openjdk64-1.8.0.272. You can install it using
brew install --cask adoptopenjdk8, or download from the website: https://adoptopenjdk.net/ (make sure to select jdk8) - We recommend you setup java using JENV
To run the Seq2Seq model, you must obtain a copy of Shazal & Usman's Seq2Seq Transliteration Tool.
git clone https://github.com/alishazal/seq2seq-transliteration-tool.git seq2seq
We ran our seq2seq systems with the GPU NVIDIA Tesla V100 PCIe 32 GB on NYU Abu Dhabi's High Performance Computing cluster, known as Dalma. We set the memory flag to 30GB. The .sh scripts that we ran can be found in the folders /src/train/seq2seq_scripts/ and /src/predict/seq2seq_scripts.
Data for this project came from publicly available catalog databases stored in the MARC (machine-readable cataloging) standard xml format. If you are only interested in replicating our experimental setup, you can skip this section as you only need the tsvs provided in data.zip under data/processed (see release files). Read on for details on downloading the original marcxml dumps, collecting Arabic records, preprocessing and splits.
Catalog data in MARC xml format were downloaded from the following sources. To re-download the sources used in this project, simply run make download_data, or run each of the following commands:
Arabic Collections Online (ACO):
git clone https://github.com/NYULibraries/aco-karms/ data/raw_records/aco/
Library of Congress (LOC):
for val in {01..43}; do wget -nc -P data/raw_records/loc https://www.loc.gov/cds/downloads/MDSConnect/BooksAll.2016.part$$val.xml.gz; done
gunzip data/raw_records/loc/*
University of Michigan (UMICH):
wget -nc -P data/raw_records/umich http://www.lib.umich.edu/files/umich_bib.xml.gz
gunzip data/raw_records/umich/*
or simply run make download_data
The first step is to read our MARC records from each data source and collect records tagged as Arabic into new marcxml collections.
UMICH and LOC are comprised of large marcxml collections containing thousands of records per file.
python3 src/data/collect_arabic_records.py data/raw_records/umich
python3 src/data/collect_arabic_records.py data/raw_records/loc
ACO records are in a parent folder named aco/work with thousands of individual xml files containing a single record each. Furthermore, each of ACO's partner institutions places their records inside a subfolder named marcxml_out, which we specify with --sub_directory_filter
python3 src/data/collect_arabic_records.py data/raw_records/aco/work --sub_directory_filter marcxml_out
Once the Arabic marcxml collections are created, you can parse them and extract parallel Arabic and Romanized entries into a single tsv by running the following command:
python3 src/loc_transcribe.py extract data/arabic_records/ data/extracted_lines/extracted_lines.tsv
or simply run make extract_lines
Finally, we cleanup data/extracted_lines/extracted_lines.tsv and split records into Train, Dev, and Test sets.
python3 src/loc_transcribe.py preprocess data/extracted_lines/extracted_lines.tsv data/processed/ --split
or simply run make data_set
This section describes how to run the various prediction models we report on in publication.
By default, predictions are run on the dev set, but you can replace the dev argument with test or a path to any tsv file containing an input column labelled ar.
The baseline model Romanizes any input Arabic text based on
- the regex rules mapped in
src/predict/ar2phon/ar2phon_map.tsv - exceptional spellings mapped in
src/predict/ar2phon/loc_exceptional_spellings.tsv.
python3 src/loc_transcribe.py predict simple dev
This model first runs MADAMIRA on the Arabic input and Romanizes the diacritized and segmented MADAMIRA output.
python3 src/loc_transcribe.py predict morph dev
The --size flag is used to specify the proportion of training data to use from the Train set. The following command will generate the 7 differently sized models we report on for MLE Simple.
python3 src/loc_transcribe.py train mle --size 1, 0.5, 0.25, 0.125, 0.0625, 0.03125, 0.015625
This will predict the dev set using the model trained on the full Training set.
- MLE Simple
python3 src/loc_transcribe.py predict mle dev --mle_model models/mle/size1.0.tsv --backoff predictions_out/simple/dev/simple.out
- MLE Morph
python3 src/loc_transcribe.py predict mle dev -m models/mle/size1.0.tsv -b predictions_out/morph/dev/morph.out
To generate predictions for all model sizes we report on, simply run make predict_mle
As mentioned, we ran the Seq2Seq model in Dalma.
This command will prepare the intermediate files and Dalma scripts required for training the 7 differently sized Seq2Seq models.
python3 src/loc_transcribe.py train seq2seq --prep --size 1.0, 0.5, 0.25, 0.125, 0.0625, 0.03125, 0.015625
or simply run make prep_seq2seq
This command will 1) train Seq2Seq models using the specified portions of the Train set and 2) predict Dev using the specified models.
python3 src/loc_transcribe.py train seq2seq --train --size 1.0, 0.5, 0.25, 0.125, 0.0625, 0.03125, 0.015625
This command generates predictions for the test set using the full Train model.
python3 src/loc_transcribe.py predict seq2seq --predict_test -s 1.0
To align Seq2Seq predictions and fill missing output using MLE Morph, we can run the following command:
python3 src/loc_transcribe.py predict seq2seq --align_backoff predictions_out/seq2seq/dev/seq2seq_size1.0.out predictions_out/mle_morph/dev/mle_morph_size1.0.out
or run make align_seq2seq and make align_seq2seq_test to replicate the predictions we report on.
- Evaluate the dev predictions of (Seq2Seq aligned with Mle Morph)
python3 src/loc_transcribe.py evaluate predictions_out/aligned_seq2seq/dev/seq2seq_size1.0Xmle_morph_size1.0.out data/processed/dev.tsv
- Evaluate the test predictions of (Seq2Seq aligned with Mle Morph)
python3 src/loc_transcribe.py evaluate predictions_out/aligned_seq2seq/test/seq2seq_size1.0Xmle_morph_size1.0.out data/processed/test.tsv
or run make evaluate to evaluate all models we report on.
To retrieve scores for all evaluated models (contained in evaluation/), run the following:
python3 src/loc_transcribe.py score all
or specify the evaluated files you want to retrieve scores for, e.g.:
python3 src/loc_transcribe.py score evaluation/dev/mle_morph_size1.0.tsv evaluation/dev/simple.tsv
