@inproceedings{bui-von-der-wense-2024-jgu,
    title = "{JGU} Mainz{'}s Submission to the {A}mericas{NLP} 2024 Shared Task on the Creation of Educational Materials for Indigenous Languages",
    author = "Bui, Minh Duc  and
      von der Wense, Katharina",
    editor = "Mager, Manuel  and
      Ebrahimi, Abteen  and
      Rijhwani, Shruti  and
      Oncevay, Arturo  and
      Chiruzzo, Luis  and
      Pugh, Robert  and
      von der Wense, Katharina",
    booktitle = "Proceedings of the 4th Workshop on Natural Language Processing for Indigenous Languages of the Americas (AmericasNLP 2024)",
    month = jun,
    year = "2024",
    address = "Mexico City, Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.americasnlp-1.23/",
    doi = "10.18653/v1/2024.americasnlp-1.23",
    pages = "195--200",
    abstract = "In this paper, we present the four systems developed by the Meenzer team from JGU for the AmericasNLP 2024 shared task on the creation of educational materials for Indigenous languages. The task involves accurately applying specific grammatical modifications to given source sentences across three low-resource Indigenous languages: Bribri, Guarani, and Maya. We train two types of model architectures: finetuning a sequence-to-sequence pointer-generator LSTM and finetuning the Mixtral 8x7B model by incorporating in-context examples into the training phase. System 1, an ensemble combining finetuned LSTMs, finetuned Mixtral models, and GPT-4, achieves the best performance on Guarani. Meanwhile, system 4, another ensemble consisting solely of fine-tuned Mixtral models, outperforms all other teams on Maya and secures the second place overall. Additionally, we conduct an ablation study to understand the performance of our system 4."
}