Papers by Gustavo Giménez-Lugo
Jojajovai: A Parallel Guarani-Spanish Corpus for MT Benchmarking (2022.lrec-1)
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Luis Chiruzzo, Santiago Góngora, Aldo Alvarez, Gustavo Giménez-Lugo, Marvin Agüero-Torales, Yliana Rodríguez
| Challenge: | a corpus of Guarani-Spanish text is presented that is aligned at sentence level . the long history of language contact between Guaran and Spanish in South America has resulted in many interesting language varieties . |
| Approach: | They propose to align Guarani-Spanish text at sentence level with 30,000 sentence pairs and a test set. |
| Outcome: | The proposed corpus contains about 30,000 sentence pairs and is structured as a collection of subsets from different sources, further split into training, development and test sets. |
AmericasNLI: Evaluating Zero-shot Natural Language Understanding of Pretrained Multilingual Models in Truly Low-resource Languages (2022.acl-long)
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Abteen Ebrahimi, Manuel Mager, Arturo Oncevay, Vishrav Chaudhary, Luis Chiruzzo, Angela Fan, John Ortega, Ricardo Ramos, Annette Rios, Ivan Vladimir Meza Ruiz, Gustavo Giménez-Lugo, Elisabeth Mager, Graham Neubig, Alexis Palmer, Rolando Coto-Solano, Thang Vu, Katharina Kann
| Challenge: | Pretrained multilingual models can perform cross-lingual transfer in a zero-shot setting, even for unseen languages. |
| Approach: | They propose to extend XNLI to 10 indigenous languages of the Americas and test multiple zero-shot and translation-based approaches. |
| Outcome: | The proposed model can perform cross-lingual transfer in a zero-shot setting even for languages unseen during pretraining. |
Meeting the Needs of Low-Resource Languages: The Value of Automatic Alignments via Pretrained Models (2023.eacl-main)
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Abteen Ebrahimi, Arya D. McCarthy, Arturo Oncevay, John E. Ortega, Luis Chiruzzo, Gustavo Giménez-Lugo, Rolando Coto-Solano, Katharina Kann
| Challenge: | Large multilingual models have inspired a new class of word alignment methods, which work well for pretraining languages. |
| Approach: | They propose to use transformer-based word alignment methods to extract alignments from massive pretrained models. |
| Outcome: | The proposed methods outperform traditional methods for languages unseen to pretraining models, and are competitive with each other. |