Muhidin Mohamed


2024

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AfriMTE and AfriCOMET: Enhancing COMET to Embrace Under-resourced African Languages
Jiayi Wang | David Adelani | Sweta Agrawal | Marek Masiak | Ricardo Rei | Eleftheria Briakou | Marine Carpuat | Xuanli He | Sofia Bourhim | Andiswa Bukula | Muhidin Mohamed | Temitayo Olatoye | Tosin Adewumi | Hamam Mokayed | Christine Mwase | Wangui Kimotho | Foutse Yuehgoh | Anuoluwapo Aremu | Jessica Ojo | Shamsuddeen Muhammad | Salomey Osei | Abdul-Hakeem Omotayo | Chiamaka Chukwuneke | Perez Ogayo | Oumaima Hourrane | Salma El Anigri | Lolwethu Ndolela | Thabiso Mangwana | Shafie Mohamed | Hassan Ayinde | Oluwabusayo Awoyomi | Lama Alkhaled | Sana Al-azzawi | Naome Etori | Millicent Ochieng | Clemencia Siro | Njoroge Kiragu | Eric Muchiri | Wangari Kimotho | Toadoum Sari Sakayo | Lyse Naomi Wamba | Daud Abolade | Simbiat Ajao | Iyanuoluwa Shode | Ricky Macharm | Ruqayya Iro | Saheed Abdullahi | Stephen Moore | Bernard Opoku | Zainab Akinjobi | Abeeb Afolabi | Nnaemeka Obiefuna | Onyekachi Ogbu | Sam Ochieng’ | Verrah Otiende | Chinedu Mbonu | Yao Lu | Pontus Stenetorp
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)

Despite the recent progress on scaling multilingual machine translation (MT) to several under-resourced African languages, accurately measuring this progress remains challenging, since evaluation is often performed on n-gram matching metrics such as BLEU, which typically show a weaker correlation with human judgments. Learned metrics such as COMET have higher correlation; however, the lack of evaluation data with human ratings for under-resourced languages, complexity of annotation guidelines like Multidimensional Quality Metrics (MQM), and limited language coverage of multilingual encoders have hampered their applicability to African languages. In this paper, we address these challenges by creating high-quality human evaluation data with simplified MQM guidelines for error detection and direct assessment (DA) scoring for 13 typologically diverse African languages. Furthermore, we develop AfriCOMET: COMET evaluation metrics for African languages by leveraging DA data from well-resourced languages and an African-centric multilingual encoder (AfroXLM-R) to create the state-of-the-art MT evaluation metrics for African languages with respect to Spearman-rank correlation with human judgments (0.441).

2023

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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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Data Expansion Using WordNet-based Semantic Expansion and Word Disambiguation for Cyberbullying Detection
Md Saroar Jahan | Djamila Romaissa Beddiar | Mourad Oussalah | Muhidin Mohamed
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Automatic identification of cyberbullying from textual content is known to be a challenging task. The challenges arise from the inherent structure of cyberbullying and the lack of labeled large-scale corpus, enabling efficient machine-learning-based tools including neural networks. This paper advocates a data augmentation-based approach that could enhance the automatic detection of cyberbullying in social media texts. We use both word sense disambiguation and synonymy relation in WordNet lexical database to generate coherent equivalent utterances of cyberbullying input data. The disambiguation and semantic expansion are intended to overcome the inherent limitations of social media posts, such as an abundance of unstructured constructs and limited semantic content. Besides, to test the feasibility, a novel protocol has been employed to collect cyberbullying traces data from AskFm forum, where about a 10K-size dataset has been manually labeled. Next, the problem of cyberbullying identification is viewed as a binary classification problem using an elaborated data augmentation strategy and an appropriate classifier. For the latter, a Convolutional Neural Network (CNN) architecture with FastText and BERT was put forward, whose results were compared against commonly employed Naïve Bayes (NB) and Logistic Regression (LR) classifiers with and without data augmentation. The research outcomes were promising and yielded almost 98.4% of classifier accuracy, an improvement of more than 4% over baseline results.

2014

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A Comparative Study of Conversion Aided Methods for WordNet Sentence Textual Similarity
Muhidin Mohamed | Mourad Oussalah
Proceedings of the First AHA!-Workshop on Information Discovery in Text

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