Papers by Jordi Armengol-Estapé
On the Multilingual Capabilities of Very Large-Scale English Language Models (2022.lrec-1)
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| Challenge: | Generative Pre-trained Transformers (GPTs) have been scaled to unprecedented sizes in the history of machine learning. |
| Approach: | They investigate the potential and limits of Generative Pre-trained Transformers in three tasks . they find it can be almost as useful for many languages as it is for English . |
| Outcome: | The proposed model can perform tasks in five different languages, and its potential is explored . it can learn from a few examples "via text interaction" and is scalable to many languages . |
Unsupervised Machine Translation in Real-World Scenarios (2022.lrec-1)
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Ona de Gibert Bonet, Iakes Goenaga, Jordi Armengol-Estapé, Olatz Perez-de-Viñaspre, Carla Parra Escartín, Marina Sanchez, Mārcis Pinnis, Gorka Labaka, Maite Melero
| Challenge: | a recent study has shown that unsupervised methods rely on monolingual corpora to build MT systems. |
| Approach: | They present the results of the MT4All CEF project using monolingual corpora . they propose to generate bilingual dictionaries and translation models from monolingual data . |
| Outcome: | The proposed method generates bilingual dictionaries and translation models from monolingual corpora . results show that it is comparable to general domain supervised translation . |
Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? A Comprehensive Assessment for Catalan (2021.findings-acl)
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Jordi Armengol-Estapé, Casimiro Pio Carrino, Carlos Rodriguez-Penagos, Ona de Gibert Bonet, Carme Armentano-Oller, Aitor Gonzalez-Agirre, Maite Melero, Marta Villegas
| Challenge: | Multilingual language models have been a crucial breakthrough for under-resourced languages . however, the superiority of language-specific models has already been proven for underresourced ones . |
| Approach: | They propose to build a monolingual monolingual model that is comparable to state-of-the-art large multilingual models. |
| Outcome: | The proposed model consistently outperforms state-of-the-art models across tasks and settings. |