Papers by Tadesse Destaw Belay
A Case Against Implicit Standards: Homophone Normalization in Machine Translation for Languages that use the Ge’ez Script. (2025.emnlp-main)
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Hellina Hailu Nigatu, Atnafu Lambebo Tonja, Henok Biadglign Ademtew, Hizkiel Mitiku Alemayehu, Negasi Haile Abadi, Tadesse Destaw Belay, Seid Muhie Yimam
| Challenge: | Homophone normalization is a pre-processing step used in Amharic natural language processing (NLP) but it also results in models that are unable to process different forms of writing in a single language. |
| Approach: | They propose a method where normalization is applied to model predictions instead of training data and a scheme where normalized data is preserved in training. |
| Outcome: | The proposed model achieves an increase in BLEU score of up to 1.03 while preserving language features in training. |
AfriHate: A Multilingual Collection of Hate Speech and Abusive Language Datasets for African Languages (2025.naacl-long)
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Shamsuddeen Hassan Muhammad, Idris Abdulmumin, Abinew Ali Ayele, David Ifeoluwa Adelani, Ibrahim Said Ahmad, Saminu Mohammad Aliyu, Paul Röttger, Abigail Oppong, Andiswa Bukula, Chiamaka Ijeoma Chukwuneke, Ebrahim Chekol Jibril, Elyas Abdi Ismail, Esubalew Alemneh, Hagos Tesfahun Gebremichael, Lukman Jibril Aliyu, Meriem Beloucif, Oumaima Hourrane, Rooweither Mabuya, Salomey Osei, Samuel Rutunda, Tadesse Destaw Belay, Tadesse Kebede Guge, Tesfa Tegegne Asfaw, Lilian Diana Awuor Wanzare, Nelson Odhiambo Onyango, Seid Muhie Yimam, Nedjma Ousidhoum
| Challenge: | Hate speech and abusive language are global phenomena that need sociocultural background knowledge to be understood, identified, and moderated. |
| Approach: | They propose to use a multilingual dataset to collect hate speech and abusive language in 15 African languages to help improve model performance. |
| Outcome: | The proposed datasets are based on tweets annotated by native speakers familiar with the regional culture and show that they perform well in low-resource settings. |
AfroXLMR-Social: Adapting Pre-trained Language Models for African Languages Social Media Text (2025.findings-emnlp)
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Tadesse Destaw Belay, Israel Abebe Azime, Ibrahim Said Ahmad, David Ifeoluwa Adelani, Idris Abdulmumin, Abinew Ali Ayele, Shamsuddeen Hassan Muhammad, Seid Muhie Yimam
| Challenge: | Domain adaptive pre-training and task-adaptive pre- training (TAPT) are popular methods to reduce this bias for low-resource languages, but they have not been explored for African multilingual encoders. |
| Approach: | They propose a large-scale social media and news domain corpus for continual pre-training on African languages. |
| Outcome: | The proposed methods improve performance on three subjective tasks, including sentiment analysis, multi-label emotion, and hate speech classification, while TAPT improves performance on other related tasks. |
EthioLLM: Multilingual Large Language Models for Ethiopian Languages with Task Evaluation (2024.lrec-main)
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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
| Challenge: | Low-resource languages are lagging behind current state-of-the-art (SOTA) developments in the field of NLP due to insufficient resources to train LLMs. |
| Approach: | They propose to use multilingual large language models for five Ethiopian languages and a benchmark dataset to evaluate their performance. |
| Outcome: | The proposed models outperform existing models in five Ethiopian languages and a benchmark dataset for various downstream NLP tasks. |
Evaluating the Capabilities of Large Language Models for Multi-label Emotion Understanding (2025.coling-main)
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Tadesse Destaw Belay, Israel Abebe Azime, Abinew Ali Ayele, Grigori Sidorov, Dietrich Klakow, Philip Slusallek, Olga Kolesnikova, Seid Muhie Yimam
| Challenge: | Emotion classification is one of the most challenging tasks in large language models. |
| Approach: | They propose to use a multi-label emotion classification dataset for four Ethiopian languages to evaluate their ability to learn and reason. |
| Outcome: | The proposed model improves the understanding of emotions in language models and how people convey emotions through various languages. |
ProverbEval: Exploring LLM Evaluation Challenges for Low-resource Language Understanding (2025.findings-naacl)
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Israel Abebe Azime, Atnafu Lambebo Tonja, Tadesse Destaw Belay, Yonas Chanie, Bontu Fufa Balcha, Negasi Haile Abadi, Henok Biadglign Ademtew, Mulubrhan Abebe Nerea, Debela Desalegn Yadeta, Derartu Dagne Geremew, Assefa Atsbiha Tesfu, Philipp Slusallek, Thamar Solorio, Dietrich Klakow
| Challenge: | Large language models (LLMs) evaluation is gaining increasing attention as they are typically trained on general-domain datasets while demonstrating notable performance on tasks out of their training domains. |
| Approach: | They propose an LLM evaluation benchmark for low-resource languages that focuses on low-rsource language understanding in culture-specific scenarios. |
| Outcome: | The proposed benchmarks outperform monolingual evaluations on proverb generation tasks and native language proverb descriptions on multiple choice tasks. |
Bridging the Culture Gap: A Framework for LLM-Driven Socio-Cultural Localization of Math Word Problems in Low-Resource Languages (2026.findings-acl)
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| Challenge: | Existing multilingual benchmarks that use translations retain English-centric entities. |
| Approach: | They propose a framework that culturally localizes translated datasets into variants enriched with local entities. |
| Outcome: | The proposed framework mitigates English-centric entity bias and improves model robustness when native entities are introduced across languages. |
CULEMO: Cultural Lenses on Emotion - Benchmarking LLMs for Cross-Cultural Emotion Understanding (2025.acl-long)
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Tadesse Destaw Belay, Ahmed Haj Ahmed, Alvin C Grissom Ii, Iqra Ameer, Grigori Sidorov, Olga Kolesnikova, Seid Muhie Yimam
| Challenge: | Existing emotion benchmarks rely on keyword-based emotion recognition, overlooking cultural dimensions required for emotion understanding. |
| Approach: | They propose a benchmark to evaluate culturally-aware emotion prediction across six languages. |
| Outcome: | The proposed benchmark evaluates state-of-the-art LLMs on culture-aware emotion prediction and sentiment analysis tasks. |