Dataset Card for dailydialogue_bn

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

More information needed

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: a string feature.
• dialogue: a List of string feature.

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

More information needed

Source Data

DailyDialogue

Initial Data Collection and Normalization

More information needed

Who are the source language producers?

More information needed

Annotations

More information needed

Annotation process

More information needed

Who are the annotators?

More information needed

Personal and Sensitive Information

More information needed

Considerations for Using the Data

Social Impact of Dataset

More information needed

Discussion of Biases

More information needed

Other Known Limitations

More information needed

Additional Information

Dataset Curators

More information needed

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.