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Datasets:
ai4bharat
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Pralekha

Tasks:
Translation
Modalities:
Text
Formats:
parquet
Languages:
Bengali
English
Gujarati
Size:
1M - 10M
ArXiv:
Tags:
parallel-corpus
document-alignment
machine-translation
Libraries:
Datasets
Dask
License:
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metadata
language:
  - bn
  - en
  - gu
  - hi
  - kn
  - ml
  - mr
  - or
  - pa
  - ta
  - te
  - ur
license: cc-by-4.0
size_categories:
  - 1M<n<10M
pretty_name: Pralekha
dataset_info:
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configs:
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    data_files:
      - split: ben
        path: alignable/ben-*
      - split: eng
        path: alignable/eng-*
      - split: guj
        path: alignable/guj-*
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        path: alignable/hin-*
      - split: kan
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        path: alignable/ori-*
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  - config_name: dev
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        path: dev/eng_hin-*
      - split: eng_kan
        path: dev/eng_kan-*
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        path: dev/eng_mal-*
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        path: dev/eng_mar-*
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        path: dev/eng_ori-*
      - split: eng_pan
        path: dev/eng_pan-*
      - split: eng_tam
        path: dev/eng_tam-*
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        path: dev/eng_tel-*
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  - config_name: test
    data_files:
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        path: test/eng_ben-*
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        path: test/eng_guj-*
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        path: test/eng_hin-*
      - split: eng_kan
        path: test/eng_kan-*
      - split: eng_mal
        path: test/eng_mal-*
      - split: eng_mar
        path: test/eng_mar-*
      - split: eng_ori
        path: test/eng_ori-*
      - split: eng_pan
        path: test/eng_pan-*
      - split: eng_tam
        path: test/eng_tam-*
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        path: test/eng_tel-*
      - split: eng_urd
        path: test/eng_urd-*
  - config_name: unalignable
    data_files:
      - split: ben
        path: unalignable/ben-*
      - split: eng
        path: unalignable/eng-*
      - split: guj
        path: unalignable/guj-*
      - split: hin
        path: unalignable/hin-*
      - split: kan
        path: unalignable/kan-*
      - split: mal
        path: unalignable/mal-*
      - split: mar
        path: unalignable/mar-*
      - split: ori
        path: unalignable/ori-*
      - split: pan
        path: unalignable/pan-*
      - split: tam
        path: unalignable/tam-*
      - split: tel
        path: unalignable/tel-*
      - split: urd
        path: unalignable/urd-*
tags:
  - parallel-corpus
  - document-alignment
  - machine-translation
task_categories:
  - translation

Pralekha: Cross-Lingual Document Alignment for Indic Languages

Pralekha is a large-scale parallel document dataset spanning across 11 Indic languages and English. It comprises over 3 million document pairs, with 1.5 million being English-centric. This dataset serves both as a benchmark for evaluating Cross-Lingual Document Alignment (CLDA) techniques and as a domain-specific parallel corpus for training document-level Machine Translation (MT) models in Indic Languages.

Dataset Description

Pralekha covers 12 languages—Bengali (ben), Gujarati (guj), Hindi (hin), Kannada (kan), Malayalam (mal), Marathi (mar), Odia (ori), Punjabi (pan), Tamil (tam), Telugu (tel), Urdu (urd), and English (eng). It includes a mixture of high- and medium-resource languages, covering 11 different scripts. The dataset spans two broad domains: News Bulletins (Indian Press Information Bureau (PIB)) and Podcast Scripts (Mann Ki Baat (MKB)), offering both written and spoken forms of data. All the data is human-written or human-verified, ensuring high quality.

While this accounts for alignable (parallel) documents, In real-world scenarios, multilingual corpora often include unalignable documents. To simulate this for CLDA evaluation, we sample unalignable documents from Sangraha Unverified, selecting 50% of Pralekha’s size to maintain a 1:2 ratio of unalignable to alignable documents.

For Machine Translation (MT) tasks, we first randomly sample 1,000 documents from the alignable subset per English-Indic language pair for each development (dev) and test set, ensuring a good distribution of varying document lengths. After excluding these sampled documents, we use the remaining documents as the training set for training document-level machine translation models.

Data Fields

Alignable & Unalignable Set:

  • n_id: Unique identifier for alignable document pairs (Random n_id's are assigned for the unalignable set.)
  • doc_id: Unique identifier for individual documents.
  • lang: Language of the document (ISO 639-3 code).
  • text: The textual content of the document.

Train, Dev & Test Set:

  • src_lang: Source Language (eng)
  • src_text: Source Language Text
  • tgt_lang: Target Language (ISO 639-3 code)
  • tgt_text: Target Language Text

Usage

You can load specific subsets and splits from this dataset using the datasets library.

Load an entire subset 

from datasets import load_dataset

dataset = load_dataset("ai4bharat/Pralekha", data_dir="<subset>")
# <subset> = alignable, unalignable, train, dev & test.

Load a specific split within a subset 

from datasets import load_dataset

dataset = load_dataset("ai4bharat/Pralekha", data_dir="<subset>/<lang>")
# <subset> = alignable, unalignable ; <lang> = ben, eng, guj, hin, kan, mal, mar, ori, pan, tam, tel, urd.

from datasets import load_dataset

dataset = load_dataset("ai4bharat/Pralekha", data_dir="<subset>/eng_<lang>")
# <subset> = train, dev & test ; <lang> = ben, guj, hin, kan, mal, mar, ori, pan, tam, tel, urd.

Data Size Statistics

Split Number of Documents Size (bytes)
Alignable 1,566,404 10,274,361,211
Unalignable 783,197 4,466,506,637
Total 2,349,601 14,740,867,848

Language-wise Statistics

Language (ISO-3) Alignable Documents Unalignable Documents Total Documents
Bengali (ben) 95,813 47,906 143,719
English (eng) 298,111 149,055 447,166
Gujarati (guj) 67,847 33,923 101,770
Hindi (hin) 204,809 102,404 307,213
Kannada (kan) 61,998 30,999 92,997
Malayalam (mal) 67,760 33,880 101,640
Marathi (mar) 135,301 67,650 202,951
Odia (ori) 46,167 23,083 69,250
Punjabi (pan) 108,459 54,229 162,688
Tamil (tam) 149,637 74,818 224,455
Telugu (tel) 110,077 55,038 165,115
Urdu (urd) 220,425 110,212 330,637

Citation

If you use Pralekha in your work, please cite us:

@article{suryanarayanan2024pralekha,
  title={Pralekha: An Indic Document Alignment Evaluation Benchmark},
  author={Suryanarayanan, Sanjay and Song, Haiyue and Khan, Mohammed Safi Ur Rahman and Kunchukuttan, Anoop and Khapra, Mitesh M and Dabre, Raj},
  journal={arXiv preprint arXiv:2411.19096},
  year={2024}
}

License

This dataset is released under the CC BY 4.0 license.

Contact

For any questions or feedback, please contact:

Please get in touch with us for any copyright concerns.