Datasets:
Tasks:
Translation
Multilinguality:
translation
Size Categories:
1M<n<10M
Language Creators:
found
Annotations Creators:
other
License:
metadata
annotations_creators:
- other
language:
- bn
- en
language_creators:
- found
license:
- cc-by-nc-sa-4.0
multilinguality:
- translation
pretty_name: BanglaNMT
size_categories:
- 1M<n<10M
source_datasets: []
tags:
- bengali
- BanglaNMT
task_categories:
- translation
Dataset Card for BanglaNMT
Table of Contents
- Dataset Card for
BanglaNMT
Dataset Description
- Repository: https://github.com/csebuetnlp/banglanmt
- Paper: "Not Low-Resource Anymore: Aligner Ensembling, Batch Filtering, and New Datasets for Bengali-English Machine Translation"
- Point of Contact: Tahmid Hasan
Dataset Summary
This is the largest Machine Translation (MT) dataset for Bengali-English, curated using novel sentence alignment methods introduced here.
Note: This is a filtered version of the original dataset that the authors used for NMT training. For the complete set, refer to the offical repository
Supported Tasks and Leaderboards
Languages
BengaliEnglish
Usage
from datasets import load_dataset
dataset = load_dataset("csebuetnlp/BanglaNMT")
Dataset Structure
Data Instances
One example from the dataset is given below in JSON format.
{
'bn': 'বিমানবন্দরে যুক্তরাজ্যে নিযুক্ত বাংলাদেশ হাইকমিশনার সাঈদা মুনা তাসনীম ও লন্ডনে বাংলাদেশ মিশনের জ্যেষ্ঠ কর্মকর্তারা তাকে বিদায় জানান।',
'en': 'Bangladesh High Commissioner to the United Kingdom Saida Muna Tasneen and senior officials of Bangladesh Mission in London saw him off at the airport.'
}
Data Fields
The data fields are as follows:
bn: astringfeature indicating the Bengali sentence.en: astringfeature indicating the English translation.
Data Splits
| split | count |
|---|---|
train |
2379749 |
validation |
597 |
test |
1000 |
Dataset Creation
Curation Rationale
Source Data
Initial Data Collection and Normalization
Who are the source language producers?
Annotations
Annotation process
Who are the annotators?
Personal and Sensitive Information
Considerations for Using the Data
Social Impact of Dataset
Discussion of Biases
Other Known Limitations
Additional Information
Dataset Curators
Licensing Information
Contents of this repository are restricted to only non-commercial research purposes under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0). Copyright of the dataset contents belongs to the original copyright holders.
Citation Information
If you use the dataset, please cite the following paper:
@inproceedings{hasan-etal-2020-low,
title = "Not Low-Resource Anymore: Aligner Ensembling, Batch Filtering, and New Datasets for {B}engali-{E}nglish Machine Translation",
author = "Hasan, Tahmid and
Bhattacharjee, Abhik and
Samin, Kazi and
Hasan, Masum and
Basak, Madhusudan and
Rahman, M. Sohel and
Shahriyar, Rifat",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-main.207",
doi = "10.18653/v1/2020.emnlp-main.207",
pages = "2612--2623",
abstract = "Despite being the seventh most widely spoken language in the world, Bengali has received much less attention in machine translation literature due to being low in resources. Most publicly available parallel corpora for Bengali are not large enough; and have rather poor quality, mostly because of incorrect sentence alignments resulting from erroneous sentence segmentation, and also because of a high volume of noise present in them. In this work, we build a customized sentence segmenter for Bengali and propose two novel methods for parallel corpus creation on low-resource setups: aligner ensembling and batch filtering. With the segmenter and the two methods combined, we compile a high-quality Bengali-English parallel corpus comprising of 2.75 million sentence pairs, more than 2 million of which were not available before. Training on neural models, we achieve an improvement of more than 9 BLEU score over previous approaches to Bengali-English machine translation. We also evaluate on a new test set of 1000 pairs made with extensive quality control. We release the segmenter, parallel corpus, and the evaluation set, thus elevating Bengali from its low-resource status. To the best of our knowledge, this is the first ever large scale study on Bengali-English machine translation. We believe our study will pave the way for future research on Bengali-English machine translation as well as other low-resource languages. Our data and code are available at https://github.com/csebuetnlp/banglanmt.",
}
Contributions
Thanks to @abhik1505040 and @Tahmid for adding this dataset.