Data
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Bengali Visual Genome (BVG for short) 1.0 has similar goals as Hindi Visual Genome (HVG) 1.1: to support the Bengali language. Bengali Visual Genome 1.0 is the multi-modal dataset in Bengali for machine translation and image
captioning. Bengali Visual Genome is a multimodal dataset consisting of text and images suitable for English-to-Bengali multimodal machine translation tasks 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 BVG, we manually translated these captions from English to Bengali taking the associated images into account. The manual translation is performed by the native Bengali speakers without referring to any machine translation system.
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 the ``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.
Dataset Formats
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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 - Bengali 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
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Dataset Segments English Words Bengali Words
---------- -------- ------------- -------------
Train 28930 143115 113978
Dev 998 4922 3936
Test 1595 7853 6408
Challenge Test 1400 8186 6657
---------- -------- ------------- -------------
Total 32923 164076 130979
The word counts are approximate, prior to tokenization.
Citation
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If you use this corpus, please cite the following paper:
@inproceedings{hindi-visual-genome:2022,
title= "{Bengali Visual Genome: A Multimodal Dataset for Machine Translation and Image Captioning}",
author={Sen, Arghyadeep
and Parida, Shantipriya
and Kotwal, Ketan
and Panda, Subhadarshi
and Bojar, Ond{\v{r}}ej
and Dash, Satya Ranjan},
editor={Satapathy, Suresh Chandra
and Peer, Peter
and Tang, Jinshan
and Bhateja, Vikrant
and Ghosh, Anumoy},
booktitle= {Intelligent Data Engineering and Analytics},
publisher= {Springer Nature Singapore},
address= {Singapore},
pages = {63--70},
isbn = {978-981-16-6624-7},
doi = {10.1007/978-981-16-6624-7_7},
}
A large web corpus (over 10 billion tokens) licensed under CreativeCommons license family in 50+ languages that has been extracted from CommonCrawl, the largest publicly available general Web crawl to date with about 2 billion crawled URLs.
A large web corpus (over 10 billion tokens) licensed under CreativeCommons license family in 50+ languages that has been extracted from CommonCrawl, the largest publicly available general Web crawl to date with about 2 billion crawled URLs.
A large web corpus (over 10 billion tokens) licensed under CreativeCommons license family in 50+ languages that has been extracted from CommonCrawl, the largest publicly available general Web crawl to date with about 2 billion crawled URLs.
A large web corpus (over 10 billion tokens) licensed under CreativeCommons license family in 50+ languages that has been extracted from CommonCrawl, the largest publicly available general Web crawl to date with about 2 billion crawled URLs.
A large web corpus (over 10 billion tokens) licensed under CreativeCommons license family in 50+ languages that has been extracted from CommonCrawl, the largest publicly available general Web crawl to date with about 2 billion crawled URLs.
A large web corpus (over 10 billion tokens) licensed under CreativeCommons license family in 50+ languages that has been extracted from CommonCrawl, the largest publicly available general Web crawl to date with about 2 billion crawled URLs.
Texts in 107 languages from the W2C corpus (http://hdl.handle.net/11858/00-097C-0000-0022-6133-9), first 1,000,000 tokens per language, tagged by the delexicalized tagger described in Yu et al. (2016, LREC, Portorož, Slovenia).
Texts in 107 languages from the W2C corpus (http://hdl.handle.net/11858/00-097C-0000-0022-6133-9), first 1,000,000 tokens per language, tagged by the delexicalized tagger described in Yu et al. (2016, LREC, Portorož, Slovenia).
Changes in version 1.1:
1. Universal Dependencies tagset instead of the older and smaller Google Universal POS tagset.
2. SVM classifier trained on Universal Dependencies 1.2 instead of HamleDT 2.0.
3. Balto-Slavic languages, Germanic languages and Romance languages were tagged by classifier trained only on the respective group of languages. Other languages were tagged by a classifier trained on all available languages. The "c7" combination from version 1.0 is no longer used.
HamleDT 2.0 is a collection of 30 existing treebanks harmonized into a common annotation style, the Prague Dependencies, and further transformed into Stanford Dependencies, a treebank annotation style that became popular recently. We use the newest basic Universal Stanford Dependencies, without added language-specific subtypes.