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The Dakshina dataset is a collection of text in both Latin and native scripts for 12 South Asian languages. For each language, the dataset includes a large collection of native script Wikipedia text, a romanization lexicon of words in the native script with attested romanizations, and some full sentence parallel data in both a native script of t…


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Dakshina Dataset

The Dakshina dataset is a collection of text in both Latin and native scripts for 12 South Asian languages. For each language, the dataset includes a large collection of native script Wikipedia text, a romanization lexicon which consists of words in the native script with attested romanizations, and some full sentence parallel data in both a native script of the language and the basic Latin alphabet.

Dataset URL:

If you use or discuss this dataset in your work, please cite our paper (bibtex citation below). A PDF link for the paper can be found at

    title = "Processing {South} {Asian} Languages Written in the {Latin} Script:
    the {Dakshina} Dataset",
    author = "Roark, Brian and
      Wolf-Sonkin, Lawrence and
      Kirov, Christo and
      Mielke, Sabrina J. and
      Johny, Cibu and
      Demir{\c{s}}ahin, I{\c{s}}in and
      Hall, Keith",
    booktitle = "Proceedings of The 12th Language Resources and Evaluation Conference (LREC)",
    year = "2020",
    url = "",
    pages = "2413--2423"

Data links

File Download Version Date Notes
dakshina_dataset_v1.0.tar link 1.0 05/27/2020 Initial data release

Data Organization

There are 12 languages represented in the dataset: Bangla (bn), Gujarati (gu), Hindi (hi), Kannada (kn), Malayalam (ml), Marathi (mr), Punjabi (pa), Sindhi (sd), Sinhala (si), Tamil (ta), Telugu (te) and Urdu (ur).

All data is derived from Wikipedia text. Each language has its own directory, in which there are three subdirectories:

Native Script Wikipedia {#native}

In the native_script_wikipedia subdirectories there are native script text strings from Wikipedia. The scripts are:

  • For bn, gu, kn, ml, si, ta and te, the scripts are named the same as the language,
  • hi and mr are in the Devanagari script,
  • pa is in the Gurmukhi script, and
  • ur and sd are in Perso-Arabic scripts.

All of the scripts other than the Perso-Arabic scripts are Brahmic. This data consists of Wikipedia strings that have been filtered (see below) to consist only of strings primarily in the Unicode codeblock for the script, plus whitespace and, in some cases, commonly used ASCII punctuation and digits. The pages from which the strings come from have been split into training and validation sets, so that no strings in the training partition come from Wikipedia pages from which validation strings are extracted. Files have been gzipped, and have accompanying information that permits linking strings back to their original Wikipedia pages. For example, the first line of mr/native_script_wikipedia/ contains:

कोल्हापुरात मिळणारा तांबडा पांढरा रस्सा कुठेच मिळत नाही.

Lexicons {#lexicons}

In the lexicons subdirectories there are lexicons of words in the native script of each language alongside human-annotated possible romanizations for the word. The words in the lexicons are all sampled from words that occurred more than once in the Wikipedia training sets, in the native_script_wikipedia subdirectories, and most received a romanization from more than one annotator, though the annotated romanizations may agree. These are in a format similar to pronunciation lexicons, i.e., single (word, romanization) pair per line in a TSV file, with an additional column indicating the number of attestations for the pair. For example, the first two lines of the file pa/lexicons/pa.translit.sampled.train.tsv contains:

ਅਂਦਾਜਾ	andaaja	1
ਅਂਦਾਜਾ	andaja	2

i.e., two different possible romanizations for the Punjabi word ਅਂਦਾਜਾ, one possible romanization (andaaja) attested once, the other (andaja) twice. For convenience, each lexicon has been partitioned into training, development and testing sets, with partitioning by native script word, so that words in the training set do not occur in the development or testing sets. In addition, we

used some automated methods to identify lemmata (see below) in each word, and ensured that lemmata in words in the development and test sets were unobserved in the training set. All native script characters -- specifically, all native script Unicode codepoints -- in the development and test sets are found in the training set. See below for further details on data elicitation and preparation. For each language there are *.train.tsv, *.dev.tsv and *.test.tsv files in the subdirectory. For all languages except for Sindhi (sd), there are 25,000 (native script) word types in the training lexicon, and 2,500 in each of the dev and test lexicons. Sindhi also has 2,500 native script word types in the dev and test lexicons, but just 15,000 in the training lexicon.

Romanized {#romanized}

In the romanized subdirectory, we have manually romanized full strings, alongside the original native script prompts for the examples. The native script prompts were selected from the validation sets in the native_script_wikipedia subdirectories (see description of preprocessing below). 10,000 strings from each native script validation set were randomly chosen to be romanized by native speaker annotators. For long sentences (more than 30 words), the sentences were segmented into shorter fragments (by splitting in half until fragments are < 30 words), and each fragment romanized independently, for ease of annotation. From this process, there are *.split.tsv and *.rejoined.tsv, which contain native script and romanized strings in the two (tab delimited) fields. (Files with 'split' are the versions with strings >= 30 segmented; those with 'rejoined' are not length segmented.) For example, the first line of hi/romanized/hi.romanized.rejoined.tsv contains:

जबकि यह जैनों से कम है।	Jabki yah Jainon se km hai.

Additionally, for convenience, we performed an automatic (white space) token-level alignment of the strings, with one aligned token per line, as well as an end-of-string marker </s>. In the case that the tokenization is not 1-1, multiple tokens are left on the same line. These alignments are provided also with the Latin script de-cased and punctuation removed, e.g., the first seven lines of the file hi/romanized/hi.romanized.rejoined.aligned.cased_nopunct.tsv are:

जबकि	jabki
यह	yah
जैनों	jainon
से	se
कम	km
है	hai
</s>	</s>

We also performed a validation of the romanizations, by requesting that different annotators transcribe the romanized strings into the native script of each language respectively (see details below). The resulting native script transcriptions are provided (*.split.validation.native.txt) for each language, along with a file (*.split.validation.edits.txt) that provides counts of (1) the total number of reference characters (in the original native-script strings), (2) substitutions, (3) deletions and (4) insertions in the validation transcriptions. For example, the first two lines of the file bn/romanized/bn.romanized.split.validation.edits.txt are:

    1 126   3   3   0

which indicates that the first native script string in bn/romanized/bn.romanized.split.tsv has 126 characters, and there were 3 substitutions, 3 deletions and 0 insertions in the native script string transcribed by annotators during the validation phase. Note that the comparison involved some script normalization of visually identical sequences to minimize spurious errors, as described in more detail below. All languages fell between 3.5 and 8.5 percent character error rates of the validation text. See below for further details on this validation process.

Finally, for convenience, we randomly shuffled this set and divided into development and test sets, each of which are broken into native and Latin script text files. Thus the first line in the file si/romanized/ is:

වැව්වල ඇළෙවිලි වැව ඉහත්තාව, වේල්ල ආරක්ෂා කිරිමට එකල සියල්ලෝම බැදි සිටියෝය.

and the first line of si/romanized/ is:

vevvala eleveli, veva ihatthava, vella araksha kirimata ekala siyalloma bendi sitiyaya.

Note that several hundred strings from the Urdu Wikipedia sample (and one from Sindhi) were not from those languages, rather from other languages using a Perso-Arabic script, e.g., Arabic, Punjabi or others. Those were excluded for those sets, leading to less than 10,000 romanized strings.

Native script data preprocessing {#native-preprocessing}

Let $L be the language code, one of bn, gu, hi, kn, ml, mr, pa, sd, si, ta, te, or ur. The native script files are in $L/native_script_wikipedia. All URLs of Wikipedia pages are included in $ This tab delimited file includes four fields: page ID, revision ID, base URL, and URL with revision ID.

We omitted whole pages that were any of the following:

  1. redirected pages.
  2. pages with infoboxes about settlements or jurisdictions.
  3. pages with state=collapsed or expanded or autocollapse
  4. pages referring to censusindia or
  5. pages with wikitable.
  6. pages with lists containing more than 7 items.

Indices of pages omitted are given in $

For pages that are not omitted, we extract text and info files:

  • $
  • $

Text is organized by page and section within page. We then:

  1. split section text by newline (leading to multiple strings per section).
  2. NFC normalize.
  3. sentence segment using ICU sentence segmentation (leading to multiple sentences per string). The ICU sentence segmenter is initialized with the locale associated with the specific language being segmented.

Both tab delimited files share the same initial 6 fields: page_id, section_index, string_index (in section), sentence_index (in string), include_bool, and text_freq, where the include_bool indicates whether to include the string or not (see below), and text_freq is the count of the full section text string in the whole collection. The latter enables us to find repeated strings as the means for determining boilerplate sections and other things to exclude.

Both files are sorted numerically (descending) by the first three fields.

The final (7th) field of $ is the text.

The remaining fields of $ are: (7) depth of the section in the page; (8) the heading level of the section; (9) the section index of the parent section; (10) the number of words in the text; (11) the number of Unicode codepoints in the text; (12) the percentage of Unicode codepoints falling in category A (described below); (13) the percentage of Unicode codepoints falling in category B (described below); and (14) the section title.

For a given native script Unicode block, we define categories A and B as follows. First, we identify a subset of codepoints as special symbols, call them non-letter symbols: non-letter ASCII codepoints; Arabic full stop; Devanagari Danda; any codepoint in the General Punctuation block; and any digits in the current native script Unicode block. Category A are those codepoints that (1) are outside of the native script Unicode block; and (2) are not in the non-letter subset of codepoints. Category B are all codepoints within the native script Unicode block.

The above-mentioned include_bool is set to true (when filtering) if: the percentage of category A is below a threshold; the percentage of category B is above a second threshold; and, finally, the percentage of whitespace-delimited words that contain at least one codepoint from the current native script Unicode block (and not in the non-letter subset) is above the same threshold as category B codepoints.

For each non-empty section title, we calculate the total number of Unicode codepoints, the total number of category A codepoints, and the fraction of codepoints that are category A, for all sections with that title. These statistics are stored in $, which is sorted in descending order by total category A codepoints. Thus, the first line of shows the section title with the most category A characters:

$ gzip -cd | head -1
6387096.000260	12141241	0.526066	सन्दर्भ

It's unsurprising the a section titled सन्दर्भ ('references') would have so much non-codeblock text (mainly ASCII). It also illustrates why we track this statistic, since we do not want to include references in the text that we are extracting. To avoid such sections, we create a list of sections where the aggregate percentage of category A codepoints in sections with that title is greater than 20%. These omitted section titles are in $

A second round of text extraction then occurs, omitting text occurring in the aforementioned sections and including only individual strings that consist of at least 85% category B codepoints, at most 10% category A codepoints, and at least 85% of white-space delimited words containing a within-codeblock (and not non-letter) codepoint.

All text that is extracted from a given Wikipedia page is collectively placed in either a training or a validation set, i.e., no strings in the validation set share a Wikipedia page with any string in the training set. Between 23 and 29 thousand strings are placed in each validation set, which represents a minimum of 2.25% of the data and a maximum of 26% of the data.

The data from this second iteration of extraction is present in:

  • $
  • $
  • $
  • $
  • $
  • $

The first three files are training set files, the final three are validation. The info.sorted and text.sorted files have index sorted data along the lines described above for the full set, for both training and validation sets. We additionally randomly shuffled the text from both sets, found in text.shuf.


Validation string selection criteria for romanization

We randomly selected 10,000 strings from the validation set detailed above for romanization by annotators. As detailed earlier, for strings with >= 30 Unicode codepoints, we segmented into shorter strings for ease of annotation.

Round-trip romanization validation {#round-trip-validation}

After eliciting manual romanizations for each of the 10,000 strings in the selected validation sets in each language, we validated the resulting romanizations via a second round of annotations, where the romanized strings were provided to annotators and they were tasked with producing the strings in the native script, which was then compared with the original string. To compare the strings, we performed a visual normalization of both the original and validation native script strings, so that visually identical strings were encoded with the same codepoints for comparison. We then calculated the number of character substitutions, deletions and insertions for each string in the Viterbi alignment between the visually normalized original and validation strings, including whitespace and punctuation. This, along with the count of characters in the reference (visually normalized original string) allows for calculation of character error rates.

As stated above, the languages all fell between 3.5 and 8.5 percent character error rate. The error rate could not have been 0 for a variety of reasons:

  • Some non-codeblock text was allowed in the original native script strings, e.g., individual words in the Latin script, something that annotators with access only to the romanizations could not recover;
  • Digit strings are typically variously realized with Latin and native script digits, which is also not recoverable;
  • In the Perso-Arabic script in particular, tokenization in native and Latin scripts may be different, leading to whitespace character mismatch; similarly, punctuation placement sometimes leads to different tokenization;
  • Errors occurring in either the original Wikipedia or validation strings;
  • Visually identical strings can be encoded with different Unicode codepoint sequences, something we controlled to some extent with visual normalization, but other correspondences may occur; and
  • Valid spelling variation exists in the languages, e.g., for English loanwords, but also for common words such as "Hindi" in Devanagari, which can be equally well realized as either हिन्दी or हिंदी.

We provide the validation strings and character edits per string to permit users of the resource to potentially explore methods that take such information into account, e.g., for model evaluation.


The dataset is licensed under CC BY-SA 4.0. Any third party content or data is provided "As Is" without any warranty, express or implied.


  • roark [at]
  • ckirov [at]
  • wolfsonkin [at]


The Dakshina dataset is a collection of text in both Latin and native scripts for 12 South Asian languages. For each language, the dataset includes a large collection of native script Wikipedia text, a romanization lexicon of words in the native script with attested romanizations, and some full sentence parallel data in both a native script of t…






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