Malayalam Visual Genome 1.0

Title:

Malayalam Visual Genome 1.0

Creator:

Parida, Shantipriya and Bojar, Ondřej

Contributor:

Czech Science Foundation@@19-26934X@@Neural Representations in Multi-modal and Multi-lingual Modelling@@nationalFunds@@

Publisher:

Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL)

Identifier:

http://hdl.handle.net/11234/1-3533

Subject:

multi-modal, neural machine translation, English-Malayalam Multimodal Corpus, Malayalam Image Captioning, and English-Malayalam Multimodal Translation

Type:

image and corpus

Description:

Data ------- 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 ------------------- 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 ----------- 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} }

Language:

English and Malayalam

Rights:

Creative Commons - Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
http://creativecommons.org/licenses/by-nc-sa/4.0/
PUB

Relation:

https://ufal.mff.cuni.cz/malayalam-visual-genome
https://www.cys.cic.ipn.mx/ojs/index.php/CyS/article/view/3294

Source:

https://ufal.mff.cuni.cz/malayalam-visual-genome

Harvested from:

LINDAT/CLARIAH-CZ repository

Metadata only:

false

Date:

2021-03-05