Papers by Axel-Cyrille Ngonga Ngomo
LOLA – An Open-Source Massively Multilingual Large Language Model (2025.coling-main)
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Nikit Srivastava, Denis Kuchelev, Tatiana Moteu Ngoli, Kshitij Shetty, Michael Roeder, Hamada Zahera, Diego Moussallem, Axel-Cyrille Ngonga Ngomo
| Challenge: | Using a sparse Mixture-of-Experts Transformer architecture, our model is highly efficient and efficient across languages. |
| Approach: | They propose a multilingual large language model trained on more than 160 languages using a sparse Mixture-of-Experts Transformer architecture. |
| Outcome: | The proposed model performs well on natural language generation and understanding tasks while avoiding the common pitfalls of multilinguality. |
LIdioms: A Multilingual Linked Idioms Data Set (L18-1)
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| Challenge: | Recent studies have focused on linguistic data sets that are bilingual on the Linguistic Linked Open Data (LLOD) 1 . |
| Approach: | They describe a multilingual RDF representation of idioms currently containing five languages . they use a model to structure the data and a method to link the data to well-known multilingual data sets such as BabelNet. |
| Outcome: | The proposed model complies with best practices according to Linguistic Linked Open Data Community. |
RDF2PT: Generating Brazilian Portuguese Texts from RDF Data (L18-1)
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Diego Moussallem, Thiago Ferreira, Marcos Zampieri, Maria Claudia Cavalcanti, Geraldo Xexéo, Mariana Neves, Axel-Cyrille Ngonga Ngomo
| Challenge: | Existing approaches to generate natural language from RDF data have been proposed to generate texts in Brazilian Portuguese. |
| Approach: | They propose a rule-based approach to verbalize RDF data to Brazilian Portuguese language. |
| Outcome: | The proposed approach generates text similar to that generated by humans and can hence be easily understood. |
REDFM: a Filtered and Multilingual Relation Extraction Dataset (2023.acl-long)
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| Challenge: | Existing Relation Extraction models rely on small datasets with low coverage of relation types . current systems rely only on small data sets with limited coverage of relationship types - especially when working with languages other than english. |
| Approach: | They propose to use an automatic annotated dataset to train relation extraction systems. |
| Outcome: | The proposed model can extract triplets in multiple languages from a human-revised dataset. |
CausalQA: A Benchmark for Causal Question Answering (2022.coling-1)
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Alexander Bondarenko, Magdalena Wolska, Stefan Heindorf, Lukas Blübaum, Axel-Cyrille Ngonga Ngomo, Benno Stein, Pavel Braslavski, Matthias Hagen, Martin Potthast
| Challenge: | Existing causal question answering datasets are relatively small and only include one type of causal question. |
| Approach: | They construct a benchmark corpus of 1.1 million causal questions with answers . they use a typology derived from a data-driven, manual analysis of QA datasets . |
| Outcome: | The proposed model achieves a ROUGE-L F1 score of 0.48 on the new QA benchmark. |
Error Analysis of Multilingual Language Models in Machine Translation: A Case Study of English-Amharic Translation (2024.emnlp-main)
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| Challenge: | Multilingual large language models have significantly advanced machine translation, yet challenges remain for low-resource languages like Amharic. |
| Approach: | They evaluated the performance of NLLB-200 and M2M in English-Amharic bidirectional translation using the Lesan AI dataset. |
| Outcome: | The proposed models outperformed the existing models in English-Amharic bidirectional translation using the Lesan AI dataset. |
Contextual Augmentation for Entity Linking using Large Language Models (2025.coling-main)
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| Challenge: | Entity Linking involves detecting and linking entity mentions in natural language texts to a knowledge graph. |
| Approach: | They propose a fine-tuned model that integrates entity recognition and disambiguation in a unified framework. |
| Outcome: | The proposed model achieves state-of-the-art on out-of domain datasets and compares with baselines. |