Gustavo Giménez-Lugo

Also published as: Gustavo Giménez Lugo


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AmericasNLI: Evaluating Zero-shot Natural Language Understanding of Pretrained Multilingual Models in Truly Low-resource Languages
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
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Pretrained multilingual models are able to perform cross-lingual transfer in a zero-shot setting, even for languages unseen during pretraining. However, prior work evaluating performance on unseen languages has largely been limited to low-level, syntactic tasks, and it remains unclear if zero-shot learning of high-level, semantic tasks is possible for unseen languages. To explore this question, we present AmericasNLI, an extension of XNLI (Conneau et al., 2018) to 10 Indigenous languages of the Americas. We conduct experiments with XLM-R, testing multiple zero-shot and translation-based approaches. Additionally, we explore model adaptation via continued pretraining and provide an analysis of the dataset by considering hypothesis-only models. We find that XLM-R’s zero-shot performance is poor for all 10 languages, with an average performance of 38.48%. Continued pretraining offers improvements, with an average accuracy of 43.85%. Surprisingly, training on poorly translated data by far outperforms all other methods with an accuracy of 49.12%.


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Findings of the AmericasNLP 2021 Shared Task on Open Machine Translation for Indigenous Languages of the Americas
Manuel Mager | Arturo Oncevay | Abteen Ebrahimi | John Ortega | Annette Rios | Angela Fan | Ximena Gutierrez-Vasques | Luis Chiruzzo | Gustavo Giménez-Lugo | Ricardo Ramos | Ivan Vladimir Meza Ruiz | Rolando Coto-Solano | Alexis Palmer | Elisabeth Mager-Hois | Vishrav Chaudhary | Graham Neubig | Ngoc Thang Vu | Katharina Kann
Proceedings of the First Workshop on Natural Language Processing for Indigenous Languages of the Americas

This paper presents the results of the 2021 Shared Task on Open Machine Translation for Indigenous Languages of the Americas. The shared task featured two independent tracks, and participants submitted machine translation systems for up to 10 indigenous languages. Overall, 8 teams participated with a total of 214 submissions. We provided training sets consisting of data collected from various sources, as well as manually translated sentences for the development and test sets. An official baseline trained on this data was also provided. Team submissions featured a variety of architectures, including both statistical and neural models, and for the majority of languages, many teams were able to considerably improve over the baseline. The best performing systems achieved 12.97 ChrF higher than baseline, when averaged across languages.


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Development of a Guarani - Spanish Parallel Corpus
Luis Chiruzzo | Pedro Amarilla | Adolfo Ríos | Gustavo Giménez Lugo
Proceedings of the 12th Language Resources and Evaluation Conference

This paper presents the development of a Guarani - Spanish parallel corpus with sentence-level alignment. The Guarani sentences of the corpus use the Jopara Guarani dialect, the dialect of Guarani spoken in Paraguay, which is based on Guarani grammar and may include several Spanish loanwords or neologisms. The corpus has around 14,500 sentence pairs aligned using a semi-automatic process, containing 228,000 Guarani tokens and 336,000 Spanish tokens extracted from web sources.