Idris Abdulmumin


2022

pdf
NaijaSenti: A Nigerian Twitter Sentiment Corpus for Multilingual Sentiment Analysis
Shamsuddeen Hassan Muhammad | David Ifeoluwa Adelani | Sebastian Ruder | Ibrahim Sa’id Ahmad | Idris Abdulmumin | Bello Shehu Bello | Monojit Choudhury | Chris Chinenye Emezue | Saheed Salahudeen Abdullahi | Anuoluwapo Aremu | Alípio Jorge | Pavel Brazdil
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Sentiment analysis is one of the most widely studied applications in NLP, but most work focuses on languages with large amounts of data. We introduce the first large-scale human-annotated Twitter sentiment dataset for the four most widely spoken languages in Nigeria—Hausa, Igbo, Nigerian-Pidgin, and Yorùbá—consisting of around 30,000 annotated tweets per language, including a significant fraction of code-mixed tweets. We propose text collection, filtering, processing and labeling methods that enable us to create datasets for these low-resource languages. We evaluate a range of pre-trained models and transfer strategies on the dataset. We find that language-specific models and language-adaptive fine-tuning generally perform best. We release the datasets, trained models, sentiment lexicons, and code to incentivize research on sentiment analysis in under-represented languages.

pdf
Hausa Visual Genome: A Dataset for Multi-Modal English to Hausa Machine Translation
Idris Abdulmumin | Satya Ranjan Dash | Musa Abdullahi Dawud | Shantipriya Parida | Shamsuddeen Muhammad | Ibrahim Sa’id Ahmad | Subhadarshi Panda | Ondřej Bojar | Bashir Shehu Galadanci | Bello Shehu Bello
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Multi-modal Machine Translation (MMT) enables the use of visual information to enhance the quality of translations, especially where the full context is not available to enable the unambiguous translation in standard machine translation. Despite the increasing popularity of such technique, it lacks sufficient and qualitative datasets to maximize the full extent of its potential. Hausa, a Chadic language, is a member of the Afro-Asiatic language family. It is estimated that about 100 to 150 million people speak the language, with more than 80 million indigenous speakers. This is more than any of the other Chadic languages. Despite the large number of speakers, the Hausa language is considered as a low resource language in natural language processing (NLP). This is due to the absence of enough resources to implement most of the tasks in NLP. While some datasets exist, they are either scarce, machine-generated or in the religious domain. Therefore, there is the need to create training and evaluation data for implementing machine learning tasks and bridging the research gap in the language. This work presents the Hausa Visual Genome (HaVG), a dataset that contains the description of an image or a section within the image in Hausa and its equivalent in English. The dataset was prepared by automatically translating the English description of the images in the Hindi Visual Genome (HVG). The synthetic Hausa data was then carefully postedited, taking into cognizance the respective images. The data is made of 32,923 images and their descriptions that are divided into training, development, test, and challenge test set. The Hausa Visual Genome is the first dataset of its kind and can be used for Hausa-English machine translation, multi-modal research, image description, among various other natural language processing and generation tasks.

pdf
A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for African News Translation
David Adelani | Jesujoba Alabi | Angela Fan | Julia Kreutzer | Xiaoyu Shen | Machel Reid | Dana Ruiter | Dietrich Klakow | Peter Nabende | Ernie Chang | Tajuddeen Gwadabe | Freshia Sackey | Bonaventure F. P. Dossou | Chris Emezue | Colin Leong | Michael Beukman | Shamsuddeen Muhammad | Guyo Jarso | Oreen Yousuf | Andre Niyongabo Rubungo | Gilles Hacheme | Eric Peter Wairagala | Muhammad Umair Nasir | Benjamin Ajibade | Tunde Ajayi | Yvonne Gitau | Jade Abbott | Mohamed Ahmed | Millicent Ochieng | Anuoluwapo Aremu | Perez Ogayo | Jonathan Mukiibi | Fatoumata Ouoba Kabore | Godson Kalipe | Derguene Mbaye | Allahsera Auguste Tapo | Victoire Memdjokam Koagne | Edwin Munkoh-Buabeng | Valencia Wagner | Idris Abdulmumin | Ayodele Awokoya | Happy Buzaaba | Blessing Sibanda | Andiswa Bukula | Sam Manthalu
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Recent advances in the pre-training for language models leverage large-scale datasets to create multilingual models. However, low-resource languages are mostly left out in these datasets. This is primarily because many widely spoken languages that are not well represented on the web and therefore excluded from the large-scale crawls for datasets. Furthermore, downstream users of these models are restricted to the selection of languages originally chosen for pre-training. This work investigates how to optimally leverage existing pre-trained models to create low-resource translation systems for 16 African languages. We focus on two questions: 1) How can pre-trained models be used for languages not included in the initial pretraining? and 2) How can the resulting translation models effectively transfer to new domains? To answer these questions, we create a novel African news corpus covering 16 languages, of which eight languages are not part of any existing evaluation dataset. We demonstrate that the most effective strategy for transferring both additional languages and additional domains is to leverage small quantities of high-quality translation data to fine-tune large pre-trained models.