Idris Abdulmumin


2024

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SemRel2024: A Collection of Semantic Textual Relatedness Datasets for 13 Languages
Nedjma Ousidhoum | Shamsuddeen Muhammad | Mohamed Abdalla | Idris Abdulmumin | Ibrahim Ahmad | Sanchit Ahuja | Alham Aji | Vladimir Araujo | Abinew Ayele | Pavan Baswani | Meriem Beloucif | Chris Biemann | Sofia Bourhim | Christine Kock | Genet Dekebo | Oumaima Hourrane | Gopichand Kanumolu | Lokesh Madasu | Samuel Rutunda | Manish Shrivastava | Thamar Solorio | Nirmal Surange | Hailegnaw Tilaye | Krishnapriya Vishnubhotla | Genta Winata | Seid Yimam | Saif Mohammad
Findings of the Association for Computational Linguistics ACL 2024

Exploring and quantifying semantic relatedness is central to representing language and holds significant implications across various NLP tasks. While earlier NLP research primarily focused on semantic similarity, often within the English language context, we instead investigate the broader phenomenon of semantic relatedness. In this paper, we present SemRel, a new semantic relatedness dataset collection annotated by native speakers across 13 languages: Afrikaans, Algerian Arabic, Amharic, English, Hausa, Hindi, Indonesian, Kinyarwanda, Marathi, Moroccan Arabic, Modern Standard Arabic, Spanish, and Telugu. These languages originate from five distinct language families and are predominantly spoken in Africa and Asia – regions characterised by a relatively limited availability of NLP resources. Each instance in the SemRel datasets is a sentence pair associated with a score that represents the degree of semantic textual relatedness between the two sentences. The scores are obtained using a comparative annotation framework. We describe the data collection and annotation processes, challenges when building the datasets, baseline experiments, and their impact and utility in NLP.

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HausaHate: An Expert Annotated Corpus for Hausa Hate Speech Detection
Francielle Vargas | Samuel Guimarães | Shamsuddeen Hassan Muhammad | Diego Alves | Ibrahim Said Ahmad | Idris Abdulmumin | Diallo Mohamed | Thiago Pardo | Fabrício Benevenuto
Proceedings of the 8th Workshop on Online Abuse and Harms (WOAH 2024)

We introduce the first expert annotated corpus of Facebook comments for Hausa hate speech detection. The corpus titled HausaHate comprises 2,000 comments extracted from Western African Facebook pages and manually annotated by three Hausa native speakers, who are also NLP experts. Our corpus was annotated using two different layers. We first labeled each comment according to a binary classification: offensive versus non-offensive. Then, offensive comments were also labeled according to hate speech targets: race, gender and none. Lastly, a baseline model using fine-tuned LLM for Hausa hate speech detection is presented, highlighting the challenges of hate speech detection tasks for indigenous languages in Africa, as well as future advances.

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Mitigating Translationese in Low-resource Languages: The Storyboard Approach
Garry Kuwanto | Eno-Abasi E. Urua | Priscilla Amondi Amuok | Shamsuddeen Hassan Muhammad | Anuoluwapo Aremu | Verrah Otiende | Loice Emma Nanyanga | Teresiah W. Nyoike | Aniefon D. Akpan | Nsima Ab Udouboh | Idongesit Udeme Archibong | Idara Effiong Moses | Ifeoluwatayo A. Ige | Benjamin Ajibade | Olumide Benjamin Awokoya | Idris Abdulmumin | Saminu Mohammad Aliyu | Ruqayya Nasir Iro | Ibrahim Said Ahmad | Deontae Smith | Praise-EL Michaels | David Ifeoluwa Adelani | Derry Tanti Wijaya | Anietie Andy
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

Low-resource languages often face challenges in acquiring high-quality language data due to the reliance on translation-based methods, which can introduce the translationese effect. This phenomenon results in translated sentences that lack fluency and naturalness in the target language. In this paper, we propose a novel approach for data collection by leveraging storyboards to elicit more fluent and natural sentences. Our method involves presenting native speakers with visual stimuli in the form of storyboards and collecting their descriptions without direct exposure to the source text. We conducted a comprehensive evaluation comparing our storyboard-based approach with traditional text translation-based methods in terms of accuracy and fluency. Human annotators and quantitative metrics were used to assess translation quality. The results indicate a preference for text translation in terms of accuracy, while our method demonstrates worse accuracy but better fluency in the language focused.

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SemEval Task 1: Semantic Textual Relatedness for African and Asian Languages
Nedjma Ousidhoum | Shamsuddeen Hassan Muhammad | Mohamed Abdalla | Idris Abdulmumin | Ibrahim Said Ahmad | Sanchit Ahuja | Alham Fikri Aji | Vladimir Araujo | Meriem Beloucif | Christine De Kock | Oumaima Hourrane | Manish Shrivastava | Thamar Solorio | Nirmal Surange | Krishnapriya Vishnubhotla | Seid Muhie Yimam | Saif M. Mohammad
Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)

We present the first shared task on Semantic Textual Relatedness (STR). While earlier shared tasks primarily focused on semantic similarity, we instead investigate the broader phenomenon of semantic relatedness across 14 languages: Afrikaans, Algerian Arabic, Amharic, English, Hausa, Hindi, Indonesian, Kinyarwanda, Marathi, Moroccan Arabic, Modern Standard Arabic, Punjabi, Spanish, and Telugu. These languages originate from five distinct language families and are predominantly spoken in Africa and Asia – regions characterised by the relatively limited availability of NLP resources. Each instance in the datasets is a sentence pair associated with a score that represents the degree of semantic textual relatedness between the two sentences. Participating systems were asked to rank sentence pairs by their closeness in meaning (i.e., their degree of semantic relatedness) in the 14 languages in three main tracks: (a) supervised, (b) unsupervised, and (c) crosslingual. The task attracted 163 participants. We received 70 submissions in total (across all tasks) from 51 different teams, and 38 system description papers. We report on the best-performing systems as well as the most common and the most effective approaches for the three different tracks.

2023

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HausaNLP at SemEval-2023 Task 10: Transfer Learning, Synthetic Data and Side-information for Multi-level Sexism Classification
Saminu Mohammad Aliyu | Idris Abdulmumin | Shamsuddeen Hassan Muhammad | Ibrahim Said Ahmad | Saheed Abdullahi Salahudeen | Aliyu Yusuf | Falalu Ibrahim Lawan
Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)

We present the findings of our participation in the SemEval-2023 Task 10: Explainable Detection of Online Sexism (EDOS) task, a shared task on offensive language (sexism) detection on English Gab and Reddit dataset. We investigated the effects of transferring two language models: XLM-T (sentiment classification) and HateBERT (same domain - Reddit) for multilevel classification into Sexist or not Sexist, and other subsequent sub-classifications of the sexist data. We also use synthetic classification of unlabelled dataset and intermediary class information to maximize the performance of our models. We submitted a system in Task A, and it ranked 49th with F1-score of 0.82. This result showed to be competitive as it only under-performed the best system by 0.052%F1-score.

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SemEval-2023 Task 12: Sentiment Analysis for African Languages (AfriSenti-SemEval)
Shamsuddeen Hassan Muhammad | Idris Abdulmumin | Seid Muhie Yimam | David Ifeoluwa Adelani | Ibrahim Said Ahmad | Nedjma Ousidhoum | Abinew Ali Ayele | Saif Mohammad | Meriem Beloucif | Sebastian Ruder
Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)

We present the first Africentric SemEval Shared task, Sentiment Analysis for African Languages (AfriSenti-SemEval) - The dataset is available at https://github.com/afrisenti-semeval/afrisent-semeval-2023. AfriSenti-SemEval is a sentiment classification challenge in 14 African languages: Amharic, Algerian Arabic, Hausa, Igbo, Kinyarwanda, Moroccan Arabic, Mozambican Portuguese, Nigerian Pidgin, Oromo, Swahili, Tigrinya, Twi, Xitsonga, and Yorb (Muhammad et al., 2023), using data labeled with 3 sentiment classes. We present three subtasks: (1) Task A: monolingual classification, which received 44 submissions; (2) Task B: multilingual classification, which received 32 submissions; and (3) Task C: zero-shot classification, which received 34 submissions. The best performance for tasks A and B was achieved by NLNDE team with 71.31 and 75.06 weighted F1, respectively. UCAS-IIE-NLP achieved the best average score for task C with 58.15 weighted F1. We describe the various approaches adopted by the top 10 systems and their approaches.

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HaVQA: A Dataset for Visual Question Answering and Multimodal Research in Hausa Language
Shantipriya Parida | Idris Abdulmumin | Shamsuddeen Hassan Muhammad | Aneesh Bose | Guneet Singh Kohli | Ibrahim Said Ahmad | Ketan Kotwal | Sayan Deb Sarkar | Ondřej Bojar | Habeebah Kakudi
Findings of the Association for Computational Linguistics: ACL 2023

This paper presents “HaVQA”, the first multimodal dataset for visual question answering (VQA) tasks in the Hausa language. The dataset was created by manually translating 6,022 English question-answer pairs, which are associated with 1,555 unique images from the Visual Genome dataset. As a result, the dataset provides 12,044 gold standard English-Hausa parallel sentences that were translated in a fashion that guarantees their semantic match with the corresponding visual information. We conducted several baseline experiments on the dataset, including visual question answering, visual question elicitation, text-only and multimodal machine translation.

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AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages
Shamsuddeen Muhammad | Idris Abdulmumin | Abinew Ayele | Nedjma Ousidhoum | David Adelani | Seid Yimam | Ibrahim Ahmad | Meriem Beloucif | Saif Mohammad | Sebastian Ruder | Oumaima Hourrane | Alipio Jorge | Pavel Brazdil | Felermino Ali | Davis David | Salomey Osei | Bello Shehu-Bello | Falalu Lawan | Tajuddeen Gwadabe | Samuel Rutunda | Tadesse Destaw Belay | Wendimu Messelle | Hailu Balcha | Sisay Chala | Hagos Gebremichael | Bernard Opoku | Stephen Arthur
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Africa is home to over 2,000 languages from over six language families and has the highest linguistic diversity among all continents. This includes 75 languages with at least one million speakers each. Yet, there is little NLP research conducted on African languages. Crucial in enabling such research is the availability of high-quality annotated datasets. In this paper, we introduce AfriSenti, a sentiment analysis benchmark that contains a total of >110,000 tweets in 14 African languages (Amharic, Algerian Arabic, Hausa, Igbo, Kinyarwanda, Moroccan Arabic, Mozambican Portuguese, Nigerian Pidgin, Oromo, Swahili, Tigrinya, Twi, Xitsonga, and Yoruba) from four language families. The tweets were annotated by native speakers and used in the AfriSenti-SemEval shared task (with over 200 participants, see website: https://afrisenti-semeval.github.io). We describe the data collection methodology, annotation process, and the challenges we dealt with when curating each dataset. We further report baseline experiments conducted on the AfriSenti datasets and discuss their usefulness.

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MasakhaNEWS: News Topic Classification for African languages
David Ifeoluwa Adelani | Marek Masiak | Israel Abebe Azime | Jesujoba Alabi | Atnafu Lambebo Tonja | Christine Mwase | Odunayo Ogundepo | Bonaventure F. P. Dossou | Akintunde Oladipo | Doreen Nixdorf | Chris Chinenye Emezue | Sana Al-azzawi | Blessing Sibanda | Davis David | Lolwethu Ndolela | Jonathan Mukiibi | Tunde Ajayi | Tatiana Moteu | Brian Odhiambo | Abraham Owodunni | Nnaemeka Obiefuna | Muhidin Mohamed | Shamsuddeen Hassan Muhammad | Teshome Mulugeta Ababu | Saheed Abdullahi Salahudeen | Mesay Gemeda Yigezu | Tajuddeen Gwadabe | Idris Abdulmumin | Mahlet Taye | Oluwabusayo Awoyomi | Iyanuoluwa Shode | Tolulope Adelani | Habiba Abdulganiyu | Abdul-Hakeem Omotayo | Adetola Adeeko | Abeeb Afolabi | Anuoluwapo Aremu | Olanrewaju Samuel | Clemencia Siro | Wangari Kimotho | Onyekachi Ogbu | Chinedu Mbonu | Chiamaka Chukwuneke | Samuel Fanijo | Jessica Ojo | Oyinkansola Awosan | Tadesse Kebede | Toadoum Sari Sakayo | Pamela Nyatsine | Freedmore Sidume | Oreen Yousuf | Mardiyyah Oduwole | Kanda Tshinu | Ussen Kimanuka | Thina Diko | Siyanda Nxakama | Sinodos Nigusse | Abdulmejid Johar | Shafie Mohamed | Fuad Mire Hassan | Moges Ahmed Mehamed | Evrard Ngabire | Jules Jules | Ivan Ssenkungu | Pontus Stenetorp
Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)

2022

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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.

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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.

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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.

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Separating Grains from the Chaff: Using Data Filtering to Improve Multilingual Translation for Low-Resourced African Languages
Idris Abdulmumin | Michael Beukman | Jesujoba Alabi | Chris Chinenye Emezue | Everlyn Chimoto | Tosin Adewumi | Shamsuddeen Muhammad | Mofetoluwa Adeyemi | Oreen Yousuf | Sahib Singh | Tajuddeen Gwadabe
Proceedings of the Seventh Conference on Machine Translation (WMT)

We participated in the WMT 2022 Large-Scale Machine Translation Evaluation for the African Languages Shared Task. This work describes our approach, which is based on filtering the given noisy data using a sentence-pair classifier that was built by fine-tuning a pre-trained language model. To train the classifier, we obtain positive samples (i.e. high-quality parallel sentences) from a gold-standard curated dataset and extract negative samples (i.e. low-quality parallel sentences) from automatically aligned parallel data by choosing sentences with low alignment scores. Our final machine translation model was then trained on filtered data, instead of the entire noisy dataset. We empirically validate our approach by evaluating on two common datasets and show that data filtering generally improves overall translation quality, in some cases even significantly.
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