Hindi Visual Genome 1.0, a multimodal dataset consisting of text and images suitable for English-to-Hindi multimodal machine translation task and multimodal research. We have selected short English segments (captions) from Visual Genome along with associated images and automatically translated them to Hindi with manual post-editing, taking the associated images into account.

The training set contains 29K segments. Further 1K and 1.6K segments are provided in a development and test sets, respectively, which follow the same (random) sampling from the original Hindi Visual Genome. Additionally, a challenge test set of 1400 segments will be released for the WAT2019 multi-modal task. This challenge test set was created by searching for (particularly) ambiguous English words based on the embedding similarity and manually selecting those where the image helps to resolve the ambiguity.

Dataset Sample

Sample items from the randomly selected segments (train/dtest/etest)

Image Image_ID X Y Width Height English Text Hindi Text
2323457 2323457 20 150 325 121 Many giraffes at a zoo एक चिड़ियाघर में कई जिराफ़
2335684 2335684 61 191 437 182 Fruit stand outside market बाजार के बाहर फल स्टैंड

Sample item from the challenge test set (chtest)

2372733 2372733 26 107 152 218 The tennis court is made up of sand and dirt य़ह टेनिस कोर्ट रेत और धूल से बना है

Sample ambiguous word: court (the key different meanings are court of justice vs. tennis court). As the image example illustrates, the word alone is ambiguous but already the English caption may contain sufficient information to disambiguate the word; the image is thus not always necessary for correct translation even in our challenge test set.

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How to cite

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},
note={In print. Presented at CICLing 2019, La Rochelle, France},
year={2019},
}