Mesay Gemeda Yigezu

Also published as: Mesay Gemeda Yigezu


2024

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EthioLLM: Multilingual Large Language Models for Ethiopian Languages with Task Evaluation
Atnafu Lambebo Tonja | Israel Abebe Azime | Tadesse Destaw Belay | Mesay Gemeda Yigezu | Moges Ahmed Ah Mehamed | Abinew Ali Ayele | Ebrahim Chekol Jibril | Michael Melese Woldeyohannis | Olga Kolesnikova | Philipp Slusallek | Dietrich Klakow | Seid Muhie Yimam
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

Large language models (LLMs) have gained popularity recently due to their outstanding performance in various downstream Natural Language Processing (NLP) tasks. However, low-resource languages are still lagging behind current state-of-the-art (SOTA) developments in the field of NLP due to insufficient resources to train LLMs. Ethiopian languages exhibit remarkable linguistic diversity, encompassing a wide array of scripts, and are imbued with profound religious and cultural significance. This paper introduces EthioLLM – multilingual large language models for five Ethiopian languages (Amharic, Ge’ez, Afan Oromo, Somali, and Tigrinya) and English, and Ethiobenchmark – a new benchmark dataset for various downstream NLP tasks. We evaluate the performance of these models across five downstream NLP tasks. We open-source our multilingual language models, new benchmark datasets for various downstream tasks, and task-specific fine-tuned language models and discuss the performance of the models. Our dataset and models are available at the https://huggingface.co/EthioNLP repository.

2023

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Habesha@DravidianLangTech: Utilizing Deep and Transfer Learning Approaches for Sentiment Analysis.
Mesay Gemeda Yigezu | Tadesse Kebede | Olga Kolesnikova | Grigori Sidorov | Alexander Gelbukh
Proceedings of the Third Workshop on Speech and Language Technologies for Dravidian Languages

This research paper focuses on sentiment analysis of Tamil and Tulu texts using a BERT model and an RNN model. The BERT model, which was pretrained, achieved satisfactory performance for the Tulu language, with a Macro F1 score of 0.352. On the other hand, the RNN model showed good performance for Tamil language sentiment analysis, obtaining a Macro F1 score of 0.208. As future work, the researchers aim to fine-tune the models to further improve their results after the training process.

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Habesha@DravidianLangTech: Abusive Comment Detection using Deep Learning Approach
Mesay Gemeda Yigezu | Selam Kanta | Olga Kolesnikova | Grigori Sidorov | Alexander Gelbukh
Proceedings of the Third Workshop on Speech and Language Technologies for Dravidian Languages

This research focuses on identifying abusive language in comments. The study utilizes deep learning models, including Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNNs), to analyze linguistic patterns. Specifically, the LSTM model, a type of RNN, is used to understand the context by capturing long-term dependencies and intricate patterns in the input sequences. The LSTM model achieves better accuracy and is enhanced through the addition of a dropout layer and early stopping. For detecting abusive language in Telugu and Tamil-English, an LSTM model is employed, while in Tamil abusive language detection, a word-level RNN is developed to identify abusive words. These models process text sequentially, considering overall content and capturing contextual dependencies.

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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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Transformer-based Model for Word Level Language Identification in Code-mixed Kannada-English Texts
Atnafu Lambebo Tonja | Mesay Gemeda Yigezu | Olga Kolesnikova | Moein Shahiki Tash | Grigori Sidorov | Alexander Gelbukh
Proceedings of the 19th International Conference on Natural Language Processing (ICON): Shared Task on Word Level Language Identification in Code-mixed Kannada-English Texts

Language Identification at the Word Level in Kannada-English Texts. This paper describes the system paper of CoLI-Kanglish 2022 shared task. The goal of this task is to identify the different languages used in CoLI-Kanglish 2022. This dataset is distributed into different categories including Kannada, English, Mixed-Language, Location, Name, and Others. This Code-Mix was compiled by CoLI-Kanglish 2022 organizers from posts on social media. We use two classification techniques, KNN and SVM, and achieve an F1-score of 0.58 and place third out of nine competitors.

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Word Level Language Identification in Code-mixed Kannada-English Texts using Deep Learning Approach
Mesay Gemeda Yigezu | Atnafu Lambebo Tonja | Olga Kolesnikova | Moein Shahiki Tash | Grigori Sidorov | Alexander Gelbukh
Proceedings of the 19th International Conference on Natural Language Processing (ICON): Shared Task on Word Level Language Identification in Code-mixed Kannada-English Texts

The goal of code-mixed language identification (LID) is to determine which language is spoken or written in a given segment of a speech, word, sentence, or document. Our task is to identify English, Kannada, and mixed language from the provided data. To train a model we used the CoLI-Kenglish dataset, which contains English, Kannada, and mixed-language words. In our work, we conducted several experiments in order to obtain the best performing model. Then, we implemented the best model by using Bidirectional Long Short Term Memory (Bi-LSTM), which outperformed the other trained models with an F1-score of 0.61%.