Papers by Diego Moussallem
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. |
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. |
NeuralREG: An end-to-end approach to referring expression generation (P18-1)
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| Challenge: | Referring Expression Generation models typically rely on features such as salience and grammatical function to make decisions about form and content. |
| Approach: | They propose a new approach that makes decisions about form and content in one go . they use a delexicalized version of the WebNLG corpus to test the approach . |
| Outcome: | The proposed approach significantly improves over two strong baselines. |