Hausa Visual Genome 1.0

Title:

Hausa Visual Genome 1.0

Creator:

Abdulmumin, Idris, Das, Satya Ranja, Dawud, Musa Abdullahi, Parida, Shantipriya, Muhammad, Shamsuddeen Hassan, Ahmad, Ibrahim Sa'id, Panda, Subhadarshi, Bojar, Ondřej, Galadanci, Bashir Shehu, and Bello, Bello Shehu

Contributor:

Czech Science Foundation@@19-26934X@@Neural Representations in Multi-modal and Multi-lingual Modelling@@nationalFunds@@, Ministerstvo školství, mládeže a tělovýchovy České republiky@@LM2018101@@LINDAT/CLARIAH-CZ: Digitální výzkumná infrastruktura pro jazykové technologie, umění a humanitní vědy@@nationalFunds@@, and Portuguese funding agency@@LA/P/0063/2020@@FCT - Fundação para a Ciência e a Tecnologia@@nationalFunds@@

Publisher:

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

Identifier:

http://hdl.handle.net/11234/1-4749

Subject:

multi-modal, machine translation, image captioning, image annotation, and neural machine translation

Type:

image and corpus

Description:

Data ------- Hausa Visual Genome 1.0, a multimodal dataset consisting of text and images suitable for English-to-Hausa 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 the dataset Hindi Visual Genome 1.1 has. We automatically translated the English captions to Hausa and manually post-edited, 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. Additionally, a challenge test set of 1400 segments is available for the multi-modal task. This challenge test set was created in Hindi Visual Genome 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 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 - Hausa 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 Hausa Words ---------- -------- ------------- ----------- Train 28930 143106 140981 Dev 998 4922 4857 Test 1595 7853 7736 Challenge Test 1400 8186 8752 ---------- -------- ------------- ----------- Total 32923 164067 162326 The word counts are approximate, prior to tokenization. Citation ----------- If you use this corpus, please cite the following paper: @InProceedings{abdulmumin-EtAl:2022:LREC, author = {Abdulmumin, Idris and Dash, Satya Ranjan and Dawud, Musa Abdullahi and Parida, Shantipriya and Muhammad, Shamsuddeen and Ahmad, Ibrahim Sa'id and Panda, Subhadarshi and Bojar, Ond{\v{r}}ej and Galadanci, Bashir Shehu and Bello, Bello Shehu}, title = "{Hausa Visual Genome: A Dataset for Multi-Modal English to Hausa Machine Translation}", booktitle = {Proceedings of the Language Resources and Evaluation Conference}, month = {June}, year = {2022}, address = {Marseille, France}, publisher = {European Language Resources Association}, pages = {6471--6479}, url = {https://aclanthology.org/2022.lrec-1.694} }

Language:

Hausa and English

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/hausa-visual-genome

Source:

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

Harvested from:

LINDAT/CLARIAH-CZ repository

Metadata only:

false

Date:

2022