@inproceedings{lupicki-etal-2025-jhus,
title = "{JHU}{'}s Submission to the {A}mericas{NLP} 2025 Shared Task on the Creation of Educational Materials for Indigenous Languages",
author = "Lupicki, Tom and
Shankar, Lavanya and
Chaparala, Kaavya and
Yarowsky, David",
editor = "Mager, Manuel and
Ebrahimi, Abteen and
Pugh, Robert and
Rijhwani, Shruti and
Von Der Wense, Katharina and
Chiruzzo, Luis and
Coto-Solano, Rolando and
Oncevay, Arturo",
booktitle = "Proceedings of the Fifth Workshop on NLP for Indigenous Languages of the Americas (AmericasNLP)",
month = may,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.americasnlp-1.12/",
doi = "10.18653/v1/2025.americasnlp-1.12",
pages = "105--111",
ISBN = "979-8-89176-236-7",
abstract = "This paper presents JHU{'}s submission to the AmericasNLP shared task on the creation of educational materials for Indigenous languages. The task involves transforming a base sentence given one or more tags that correspond to grammatical features, such as negation or tense. The task also spans four languages: Bribri, Maya, Guaran{\'i}, and Nahuatl. We experiment with augmenting prompts to large language models with different information, chain of thought prompting, ensembling large language models by majority voting, and training a pointer-generator network. Our System 1, an ensemble of large language models, achieves the best performance on Maya and Guaran{\'i}, building upon the previous successes in leveraging large language models for this task and highlighting the effectiveness of ensembling large language models."
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<abstract>This paper presents JHU’s submission to the AmericasNLP shared task on the creation of educational materials for Indigenous languages. The task involves transforming a base sentence given one or more tags that correspond to grammatical features, such as negation or tense. The task also spans four languages: Bribri, Maya, Guaraní, and Nahuatl. We experiment with augmenting prompts to large language models with different information, chain of thought prompting, ensembling large language models by majority voting, and training a pointer-generator network. Our System 1, an ensemble of large language models, achieves the best performance on Maya and Guaraní, building upon the previous successes in leveraging large language models for this task and highlighting the effectiveness of ensembling large language models.</abstract>
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%0 Conference Proceedings
%T JHU’s Submission to the AmericasNLP 2025 Shared Task on the Creation of Educational Materials for Indigenous Languages
%A Lupicki, Tom
%A Shankar, Lavanya
%A Chaparala, Kaavya
%A Yarowsky, David
%Y Mager, Manuel
%Y Ebrahimi, Abteen
%Y Pugh, Robert
%Y Rijhwani, Shruti
%Y Von Der Wense, Katharina
%Y Chiruzzo, Luis
%Y Coto-Solano, Rolando
%Y Oncevay, Arturo
%S Proceedings of the Fifth Workshop on NLP for Indigenous Languages of the Americas (AmericasNLP)
%D 2025
%8 May
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-236-7
%F lupicki-etal-2025-jhus
%X This paper presents JHU’s submission to the AmericasNLP shared task on the creation of educational materials for Indigenous languages. The task involves transforming a base sentence given one or more tags that correspond to grammatical features, such as negation or tense. The task also spans four languages: Bribri, Maya, Guaraní, and Nahuatl. We experiment with augmenting prompts to large language models with different information, chain of thought prompting, ensembling large language models by majority voting, and training a pointer-generator network. Our System 1, an ensemble of large language models, achieves the best performance on Maya and Guaraní, building upon the previous successes in leveraging large language models for this task and highlighting the effectiveness of ensembling large language models.
%R 10.18653/v1/2025.americasnlp-1.12
%U https://aclanthology.org/2025.americasnlp-1.12/
%U https://doi.org/10.18653/v1/2025.americasnlp-1.12
%P 105-111
Markdown (Informal)
[JHU’s Submission to the AmericasNLP 2025 Shared Task on the Creation of Educational Materials for Indigenous Languages](https://aclanthology.org/2025.americasnlp-1.12/) (Lupicki et al., AmericasNLP 2025)
ACL