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-Indic Pairs. 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.

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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.

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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

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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.

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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

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Citation

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

@inproceedings{suryanarayanan-etal-2025-pralekha,
    title = "{PRALEKHA}: Cross-Lingual Document Alignment for {I}ndic Languages",
    author = "Suryanarayanan, Sanjay  and Song, Haiyue  and Khan, Mohammed Safi Ur Rahman  and Kunchukuttan, Anoop  and Dabre, Raj",
    booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.ijcnlp-long.37/",
    pages = "662--676"
    }

License

This dataset is released under the CC BY 4.0 license.

Contact

For any questions or feedback, please contact:

• Sanjay Suryanarayanan (sanj.ai@outlook.com)
• Haiyue Song (haiyue.song@nict.go.jp)
• Raj Dabre (raj.dabre@cse.iitm.ac.in)

Please get in touch with us for any copyright concerns.