Datasets:
annotations_creators:
- machine-generated
language_creators:
- found
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- extended
task_categories:
- conversational
- text-generation
- text2text-generation
language:
- bn
license:
- cc-by-nc-sa-4.0
Dataset Card for dailydialogue_bn
Table of Contents
- Dataset Card for
dailydialogue_bn
Dataset Description
- Repository: https://github.com/csebuetnlp/BanglaNLG
- Paper: "BanglaNLG and BanglaT5: Benchmarks and Resources for Evaluating Low-Resource Natural Language Generation in Bangla"
- Point of Contact: Tahmid Hasan
Dataset Summary
This is a Multi-turn dialogue dataset for Bengali, curated from the original English DailyDialogue dataset and using the state-of-the-art English to Bengali translation model introduced here.
Supported Tasks and Leaderboards
Languages
Bengali
Usage
from datasets import load_dataset
dataset = load_dataset("csebuetnlp/dailydialogue_bn")
Dataset Structure
Data Instances
One example from the dataset is given below in JSON format. Each element of the dialogue feature represents a single turn of the conversation.
{
"id": "130",
"dialogue":
[
"তোমার জন্মদিনের জন্য তুমি কি করবে?",
"আমি আমার বন্ধুদের সাথে পিকনিক করতে চাই, মা।",
"বাড়িতে পার্টি হলে কেমন হয়? এভাবে আমরা একসাথে হয়ে উদযাপন করতে পারি।",
"ঠিক আছে, মা। আমি আমার বন্ধুদের বাড়িতে আমন্ত্রণ জানাবো।"
]
}
Data Fields
The data fields are as follows:
id: astringfeature.dialogue: a List ofstringfeature.
Data Splits
| split | count |
|---|---|
train |
11118 |
validation |
1000 |
test |
1000 |
Dataset Creation
For the training set, we translated the complete DailyDialogue dataset using the English to Bangla translation model introduced here. Due to the possibility of incursions of error during automatic translation, we used the Language-Agnostic BERT Sentence Embeddings (LaBSE) of the translations and original sentences to compute their similarity. A datapoint was accepted if all of its constituent sentences had a similarity score over 0.7.
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{bhattacharjee-etal-2023-banglanlg,
title = "{B}angla{NLG} and {B}angla{T}5: Benchmarks and Resources for Evaluating Low-Resource Natural Language Generation in {B}angla",
author = "Bhattacharjee, Abhik and
Hasan, Tahmid and
Ahmad, Wasi Uddin and
Shahriyar, Rifat",
booktitle = "Findings of the Association for Computational Linguistics: EACL 2023",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-eacl.54",
pages = "726--735",
abstract = "This work presents {`}BanglaNLG,{'} a comprehensive benchmark for evaluating natural language generation (NLG) models in Bangla, a widely spoken yet low-resource language. We aggregate six challenging conditional text generation tasks under the BanglaNLG benchmark, introducing a new dataset on dialogue generation in the process. Furthermore, using a clean corpus of 27.5 GB of Bangla data, we pretrain {`}BanglaT5{'}, a sequence-to-sequence Transformer language model for Bangla. BanglaT5 achieves state-of-the-art performance in all of these tasks, outperforming several multilingual models by up to 9{\%} absolute gain and 32{\%} relative gain. We are making the new dialogue dataset and the BanglaT5 model publicly available at https://github.com/csebuetnlp/BanglaNLG in the hope of advancing future research on Bangla NLG.",
}
Contributions
Thanks to @abhik1505040 and @Tahmid for adding this dataset.