@inproceedings{ahmed-etal-2023-enhancing,
    title = "Enhancing {S}panish-{Q}uechua Machine Translation with Pre-Trained Models and Diverse Data Sources: {LCT}-{EHU} at {A}mericas{NLP} Shared Task",
    author = "Ahmed, Nouman  and
      Flechas Manrique, Natalia  and
      Petrovi{\'c}, Antonije",
    editor = "Mager, Manuel  and
      Ebrahimi, Abteen  and
      Oncevay, Arturo  and
      Rice, Enora  and
      Rijhwani, Shruti  and
      Palmer, Alexis  and
      Kann, Katharina",
    booktitle = "Proceedings of the Workshop on Natural Language Processing for Indigenous Languages of the Americas (AmericasNLP)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.americasnlp-1.16",
    doi = "10.18653/v1/2023.americasnlp-1.16",
    pages = "156--162",
    abstract = "We present the LCT-EHU submission to the AmericasNLP 2023 low-resource machine translation shared task. We focus on the Spanish-Quechua language pair and explore the usage of different approaches: (1) Obtain new parallel corpora from the literature and legal domains, (2) Compare a high-resource Spanish-English pre-trained MT model with a Spanish-Finnish pre-trained model (with Finnish being chosen as a target language due to its morphological similarity to Quechua), and (3) Explore additional techniques such as copied corpus and back-translation. Overall, we show that the Spanish-Finnish pre-trained model outperforms other setups, while low-quality synthetic data reduces the performance.",
}

We present the LCT-EHU submission to the AmericasNLP 2023 low-resource machine translation shared task. We focus on the Spanish-Quechua language pair and explore the usage of different approaches: (1) Obtain new parallel corpora from the literature and legal domains, (2) Compare a high-resource Spanish-English pre-trained MT model with a Spanish-Finnish pre-trained model (with Finnish being chosen as a target language due to its morphological similarity to Quechua), and (3) Explore additional techniques such as copied corpus and back-translation. Overall, we show that the Spanish-Finnish pre-trained model outperforms other setups, while low-quality synthetic data reduces the performance.
Ahmed, Nouman Flechas Manrique, Natalia Petrovi{\'c}, Antonije
Enhancing Spanish-Quechua Machine Translation with Pre-Trained Models and Diverse Data Sources: LCT-EHU at AmericasNLP Shared Task