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Fix `license` metadata (#1)
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metadata
annotations_creators:
  - no-annotation
language_creators:
  - found
language:
  - as
  - bn
  - gu
  - hi
  - kn
  - ml
  - mr
  - or
  - pa
  - ta
  - te
license:
  - cc-by-nc-4.0
multilinguality:
  - multilingual
pretty_name: IndicParaphrase
size_categories:
  - 1M<n<10M
source_datasets:
  - original
task_categories:
  - conditional-text-generation
task_ids:
  - conditional-text-generation-other-paraphrase-generation

Dataset Card for "IndicParaphrase"

Table of Contents

Dataset Description

Dataset Summary

IndicParaphrase is the paraphrasing dataset released as part of IndicNLG Suite. Each input is paired with up to 5 references. We create this dataset in eleven languages including as, bn, gu, hi, kn, ml, mr, or, pa, ta, te. The total size of the dataset is 5.57M.

Supported Tasks and Leaderboards

Tasks: Paraphrase generation

Leaderboards: Currently there is no Leaderboard for this dataset.

Languages

  • Assamese (as)
  • Bengali (bn)
  • Gujarati (gu)
  • Kannada (kn)
  • Hindi (hi)
  • Malayalam (ml)
  • Marathi (mr)
  • Oriya (or)
  • Punjabi (pa)
  • Tamil (ta)
  • Telugu (te)

Dataset Structure

Data Instances

One example from the hi dataset is given below in JSON format.

{
  'id': '1',
 'input': 'निजी क्षेत्र में प्रदेश की 75 प्रतिशत नौकरियां हरियाणा के युवाओं के लिए आरक्षित की जाएगी।',
 'references': ['प्रदेश के युवाओं को निजी उद्योगों में 75 प्रतिशत आरक्षण देंगे।',
'युवाओं के लिए हरियाणा की सभी प्राइवेट नौकरियों में 75 प्रतिशत आरक्षण लागू किया जाएगा।',
'निजी क्षेत्र में 75 प्रतिशत आरक्षित लागू कर प्रदेश के युवाओं का रोजगार सुनिश्चत किया जाएगा।',
'प्राईवेट कम्पनियों में हरियाणा के नौजवानों को 75 प्रतिशत नौकरियां में आरक्षित की जाएगी।',
'प्रदेश की प्राइवेट फैक्टरियों में 75 फीसदी रोजगार हरियाणा के युवाओं के लिए आरक्षित किए जाएंगे।'],
 'target': 'प्रदेश के युवाओं को निजी उद्योगों में 75 प्रतिशत आरक्षण देंगे।'
}

Data Fields

  • id (string): Unique identifier.
  • pivot (string): English sentence used as the pivot
  • input (string): Input sentence
  • references (list of strings): Paraphrases of input, ordered according to the least n-gram overlap
  • target (string): The first reference (most dissimilar paraphrase)

Data Splits

We first select 10K instances each for the validation and test and put remaining in the training dataset. Assamese (as), due to its low-resource nature, could only be split into validation and test sets with 4,420 examples each. Individual dataset with train-dev-test example counts are given below:

Language ISO 639-1 Code Train Dev Test
Assamese as - 4,420 4,420
Bengali bn 890,445 10,000 10,000
Gujarati gu 379,202 10,000 10,000
Hindi hi 929,507 10,000 10,000
Kannada kn 522,148 10,000 10,000
Malayalam ml 761,933 10,000 10,000
Marathi mr 406,003 10,000 10,000
Oriya or 105,970 10,000 10,000
Punjabi pa 266,704 10,000 10,000
Tamil ta 497,798 10,000 10,000
Telugu te 596,283 10,000 10,000

Dataset Creation

Curation Rationale

[More information needed]

Source Data

Samanantar dataset

Initial Data Collection and Normalization

Detailed in the paper

Who are the source language producers?

Detailed in the paper

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 4.0 International License (CC BY-NC 4.0). Copyright of the dataset contents belongs to the original copyright holders.

Citation Information

If you use any of the datasets, models or code modules, please cite the following paper:

@inproceedings{Kumar2022IndicNLGSM,
  title={IndicNLG Suite: Multilingual Datasets for Diverse NLG Tasks in Indic Languages},
  author={Aman Kumar and Himani Shrotriya and Prachi Sahu and Raj Dabre and Ratish Puduppully and Anoop Kunchukuttan and Amogh Mishra and Mitesh M. Khapra and Pratyush Kumar},
  year={2022},
  url = "https://arxiv.org/abs/2203.05437"
}  

Contributions