HinDialect: 26 Hindi-related languages and dialects of the Indic Continuum in North India
Languages
This is a collection of folksongs for 26 languages that form a dialect continuum in North India and nearby regions.
Namely Angika, Awadhi, Baiga, Bengali, Bhadrawahi, Bhili, Bhojpuri, Braj, Bundeli, Chhattisgarhi, Garhwali, Gujarati, Haryanvi, Himachali, Hindi, Kanauji, Khadi Boli, Korku, Kumaoni, Magahi, Malvi, Marathi, Nimadi, Panjabi, Rajasthani, Sanskrit.
This data is originally collected by the Kavita Kosh Project at http://www.kavitakosh.org/ . Here are the main characteristics of the languages in this collection:
- They are all Indic languages except for Korku.
- The majority of them are closely related to the standard Hindi dialect genealogically (such as Hariyanvi and Bhojpuri), although the collection also contains languages such as Bengali and Gujarati which are more distant relatives.
- They are all primarily spoken in (North) India (Bengali is also spoken in Bangladesh)
- All except Sanksrit are alive languages
Data
Categorising them by pre-existing available NLP resources, we have:
* Band 1 languages : Hindi, Panjabi, Gujarati, Bengali, Nepali. These languages already have other large standard datasets available. Kavita Kosh may have very little data for these languages.
* Band 2 languages: Bhojpuri, Magahi, Awadhi, Braj. These languages have growing interest and some datasets of a relatively small size as compared to Band 1 language resources.
* Band 3 languages: All other languages in the collection are previously zero-resource languages. These are the languages for which this dataset is the most relevant.
Script
This dataset is entirely in Devanagari. Content in the case of languages not written in Devanagari (such as Bengali and Gujarati) has been transliterated by the Kavita Kosh Project.
Format
The dataset contains a single text file containing folksongs per language. Folksongs are separated from each other by an empty line. The first line of a new piece is the title of the folksong, and line separation within folksongs is preserved.
HinDialect: 26 Hindi-related languages and dialects of the Indic Continuum in North India
Languages
This is a collection of folksongs for 26 languages that form a dialect continuum in North India and nearby regions.
Namely Angika, Awadhi, Baiga, Bengali, Bhadrawahi, Bhili, Bhojpuri, Braj, Bundeli, Chhattisgarhi, Garhwali, Gujarati, Haryanvi, Himachali, Hindi, Kanauji, Khadi Boli, Korku, Kumaoni, Magahi, Malvi, Marathi, Nimadi, Panjabi, Rajasthani, Sanskrit.
This data is originally collected by the Kavita Kosh Project at http://www.kavitakosh.org/ . Here are the main characteristics of the languages in this collection:
- They are all Indic languages except for Korku.
- The majority of them are closely related to the standard Hindi dialect genealogically (such as Hariyanvi and Bhojpuri), although the collection also contains languages such as Bengali and Gujarati which are more distant relatives.
- All except Nepali are primarily spoken in (North) India
- All except Sanksrit are alive languages
Data
Categorising them by pre-existing available NLP resources, we have:
* Band 1 languages : Hindi, Marathi, Punjabi, Sindhi, Gujarati, Bengali, Nepali. These languages already have other large datasets available. Since Kavita Kosh focusses largely on Hindi-related languages, we may have very little data for these other languages in this particular dataset.
* Band 2 languages: Bhojpuri, Magahi, Awadhi, Brajbhasha. These languages have growing interest and some datasets of a relatively small size as compared to Band 1 language resources.
* Band 3 languages: All other languages in the collection are previously zero-resource languages. These are the languages for which this dataset is the most relevant.
Script
This dataset is entirely in Devanagari. Content in the case of languages not written in Devanagari (such as Bengali and Gujarati) has been transliterated by the Kavita Kosh Project.
Format
The data is segregated by language, and contains each folksong in a different JSON file.
KUK 0.0 is a pilot version of a corpus of Czech legal and administrative texts designated as data for manual and automatic assessment of accessibility (comprehensibility or clarity) of Czech legal texts.
GeCzLex 1.0 is an online electronic resource for translation equivalents of Czech and German discourse connectives. It contains anaphoric connectives for both languages and their possible translations documented in bilingual parallel corpora (not necessarily anaphoric). The entries have been interlinked via semantic annotation of the connectives (taken from monolingual lexicons of connectives CzeDLex and DiMLex) according to the PDTB 3 sense taxonomy and translation possibilities aquired from the Czech and German parallel data of the Intercorp project. The lexicon is the first bilingual inventory of connectives with linkage on the level of individual pairs (connective + discourse sense).
Data
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Malayalam Visual Genome (MVG for short) 1.0 has similar goals as Hindi Visual Genome (HVG) 1.1: to support the Malayalam language. Malayalam Visual Genome 1.0 is the first multi-modal dataset in Malayalam for machine translation and image captioning.
Malayalam Visual Genome 1.0 serves in "WAT 2021 Multi-Modal Machine Translation Task".
Malayalam Visual Genome is a multimodal dataset consisting of text and images suitable for English-to-Malayalam multimodal machine translation task and multimodal research. We follow the same selection of short English segments (captions) and the associated images from Visual Genome as HGV 1.1 has. For MVG, we automatically translated these captions from English to Malayalam and manually corrected them, taking the associated images into account.
The training set contains 29K segments. Further 1K and 1.6K segments are provided in development and test sets, respectively, which follow the same (random) sampling from the original Hindi Visual Genome.
A third test set is called ``challenge test set'' and consists of 1.4K segments. The challenge test set was created for the WAT2019 multi-modal task by searching for (particularly) ambiguous English words based on the embedding similarity and manually selecting those where the image helps to resolve the ambiguity. The surrounding words in the sentence however also often include sufficient cues to identify the correct meaning of the ambiguous word. For MVG, we simply translated the English side of the test sets to Malayalam, again utilizing machine translation to speed up the process.
Dataset Formats
----------------------
The multimodal dataset contains both text and images.
The text parts of the dataset (train and test sets) are in simple tab-delimited plain text files.
All the text files have seven columns as follows:
Column1 - image_id
Column2 - X
Column3 - Y
Column4 - Width
Column5 - Height
Column6 - English Text
Column7 - Malayalam Text
The image part contains the full images with the corresponding image_id as the file name. The X, Y, Width and Height columns indicate the rectangular region in the image described by the caption.
Data Statistics
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The statistics of the current release are given below.
Parallel Corpus Statistics
---------------------------------
Dataset Segments English Words Malayalam Words
---------- -------------- -------------------- -----------------
Train 28930 143112 107126
Dev 998 4922 3619
Test 1595 7853 5689
Challenge Test 1400 8186 6044
-------------------- ------------ ------------------ ------------------
Total 32923 164073 122478
The word counts are approximate, prior to tokenization.
Citation
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If you use this corpus, please cite the following paper:
@article{hindi-visual-genome:2019, title={{Hindi Visual Genome: A Dataset for Multimodal English-to-Hindi Machine Translation}}, author={Parida, Shantipriya and Bojar, Ond{\v{r}}ej and Dash, Satya Ranjan}, journal={Computaci{\'o}n y Sistemas}, volume={23}, number={4}, pages={1499--1505}, year={2019} }
En-De translation models, exported via TensorFlow Serving, available in the Lindat translation service (https://lindat.mff.cuni.cz/services/translation/).
The models were trained using the MCSQ social surveys dataset (available at https://repo.clarino.uib.no/xmlui/bitstream/handle/11509/142/mcsq_v3.zip).
Their main use should be in-domain translation of social surveys.
Models are compatible with Tensor2tensor version 1.6.6.
For details about the model training (data, model hyper-parameters), please contact the archive maintainer.
Evaluation on MCSQ test set (BLEU):
en->de: 67.5 (train: genuine in-domain MCSQ data only)
de->en: 75.0 (train: additional in-domain backtranslated MCSQ data)
(Evaluated using multeval: https://github.com/jhclark/multeval)
En-Ru translation models, exported via TensorFlow Serving, available in the Lindat translation service (https://lindat.mff.cuni.cz/services/translation/).
The models were trained using the MCSQ social surveys dataset (available at https://repo.clarino.uib.no/xmlui/bitstream/handle/11509/142/mcsq_v3.zip).
Their main use should be in-domain translation of social surveys.
Models are compatible with Tensor2tensor version 1.6.6.
For details about the model training (data, model hyper-parameters), please contact the archive maintainer.
Evaluation on MCSQ test set (BLEU):
en->ru: 64.3 (train: genuine in-domain MCSQ data)
ru->en: 74.7 (train: additional backtranslated in-domain MCSQ data)
(Evaluated using multeval: https://github.com/jhclark/multeval)
This package provides an evaluation framework, training and test data for semi-automatic recognition of sections of historical diplomatic manuscripts. The data collection consists of 57 Latin charters issued by the Royal Chancellery of 7 different types. Documents were created in the era of John the Blind, King of Bohemia (1310–1346) and Count of Luxembourg. Manuscripts were digitized, transcribed, and typical sections of medieval charters ('corroboratio', 'datatio', 'dispositio', 'inscriptio', 'intitulatio', 'narratio', and 'publicatio') were manually tagged. Manuscripts also contain additional metadata, such as manually marked named entities and short Czech abstracts.
Recognition models are first trained using manually marked sections in training documents and the trained model can then be used for recognition of the sections in the test data. The parsing script supports methods based on Cosine Distance, TF-IDF weighting and adapted Viterbi algorithm.
Migrant Stories is a corpus of 1017 short biographic narratives of migrants supplemented with meta information about countries of origin/destination, the migrant gender, GDP per capita of the respective countries, etc. The corpus has been compiled as a teaching material for data analysis.
Czech morphological dictionary developed originally by Jan Hajič as a spelling checker and lemmatization dictionary. Currently it contains full morphological information for each covered wordform, as well as some derivational, semantic and named entity information.