@article{mager2023ethical,
  title={Ethical considerations for machine translation of indigenous languages: Giving a voice to the speakers},
  author={Mager, Manuel and Mager, Elisabeth and Kann, Katharina and Vu, Ngoc Thang},
  journal={arXiv preprint arXiv:2305.19474},
  year={2023}
}
@book{garvin1972urbanization,
  title={The urbanization of the Guarani language: a problem in language and culture},
  author={Garvin, Paul L and Mathiot, Madeleine},
  year={1972},
  publisher={State University of New York et Buffalo}
}

@article{woodbury2012endangered,
  title={What is an endangered language},
  author={Woodbury, Anthony C},
  journal={Linguistic society of America},
  year={2012}
}

@misc{rodriguez2022challenges,
  author       = {Yliana Rodr{\'i}guez and Luis Chiruzzo and Santiago G{\'o}ngora},
  title        = {The challenges of creating a corpus of minority languages and its dialects in Natural Language Processing: the case of the South American indigenous language Guarani},
  howpublished = {Presented at the 32nd Meeting of Computational Linguistics in The Netherlands (CLIN32)},
  month        = {June},
  year         = {2022},
  url          = {https://clin2022.tshdresearch.org/the-challenges-of-creating-a-corpus-of-minority-languages-and-its-dialects-in-natural-language-processing-the-case-of-the-south-american-indigenous-language-guarani/}
}
@inproceedings{sennrich2016improving,
  title={Improving Neural Machine Translation Models with Monolingual Data},
  author={Sennrich, Rico and Haddow, Barry and Birch, Alexandra},
  booktitle={Proceedings of ACL},
  year={2016}
}

@inproceedings{tang2020multilingual,
  title={Multilingual Translation with Extensible Multilingual Pretraining and Finetuning},
  author={Tang, Yinhan and et al.},
  booktitle={Proceedings of EMNLP},
  year={2020}
}
@article{costa2022no,
  title={No language left behind: Scaling human-centered machine translation},
  author={Costa-Juss{\`a}, Marta R and Cross, James and {\c{C}}elebi, Onur and Elbayad, Maha and Heafield, Kenneth and Heffernan, Kevin and Kalbassi, Elahe and Lam, Janice and Licht, Daniel and Maillard, Jean and others},
  journal={arXiv preprint arXiv:2207.04672},
  year={2022}
}

@inproceedings{mager2021findings,
  title={Findings of the AmericasNLP 2021 Shared Task on Open Machine Translation for Indigenous Languages of the Americas},
  author={Mager, Manuel and Oncevay, Arturo and others},
  booktitle={Proceedings of the AmericasNLP Workshop},
  year={2021}
}

@inproceedings{ebrahimi2023findings,
  title={Findings of the AmericasNLP 2023 Shared Task on Machine Translation into Indigenous Languages},
  author={Ebrahimi, Abteen and Mager, Manuel and et al.},
  booktitle={Proceedings of the AmericasNLP Workshop},
  year={2023}
}

@inproceedings{ebrahimi2024findings,
  title={Findings of the AmericasNLP 2024 Shared Task on Machine Translation into Indigenous Languages},
  author={Ebrahimi, Abteen and others},
  booktitle={Proceedings of the AmericasNLP Workshop},
  year={2024}
}

@inproceedings{gow2023sheffield,
  title        = {Sheffield's Submission to the AmericasNLP Shared Task on Machine Translation into Indigenous Languages},
  author       = {Gow-Smith, Edward and Sánchez Villegas, Danae},
  booktitle    = {Proceedings of the Third Workshop on NLP for Indigenous Languages of the Americas (AmericasNLP)},
  year         = {2023},
  url          = {https://arxiv.org/abs/2306.09830}
}

@inproceedings{tonja2023enhancing,
  title        = {Enhancing Translation for Indigenous Languages: Experiments with Multilingual Models},
  author       = {Tonja, Atnafu Lambebo and Nigatu, Hellina Hailu and Kolesnikova, Olga and Sidorov, Grigori and Gelbukh, Alexander and Kalita, Jugal},
  booktitle    = {Proceedings of the Third Workshop on NLP for Indigenous Languages of the Americas (AmericasNLP)},
  year         = {2023},
  url          = {https://arxiv.org/abs/2305.17406}
}

@inproceedings{nagoudi2021indt5,
  title        = {IndT5: A Text-to-Text Transformer for 10 Indigenous Languages},
  author       = {Nagoudi, El Moatez Billah and Guti{\'e}rrez-V{\'a}squez, Yustin and Oncevay, Arturo and Mager, Manuel and Tannier, Xavier and Seddah, Djam{\'e}},
  booktitle    = {Proceedings of the First Workshop on NLP for Indigenous Languages of the Americas (AmericasNLP)},
  year         = {2021},
  url          = {https://arxiv.org/abs/2104.07483}
}



@inproceedings{castrillon2020towards,
  title={Towards a Guarani-Spanish Bilingual Corpus for Machine Translation},
  author={Castrill{\'o}n, Jos{\'e} Luis and Chiruzzo, Luis and Aguilar, Gustavo},
  booktitle={Proceedings of the Second Workshop on NLP for Indigenous Languages of the Americas (AmericasNLP)},
  year={2020},
  url={https://aclanthology.org/2020.americasnlp-1.3}
}

@misc{driggersellis2025multiscript30kleveragingmultilingualembeddings,
      title={MultiScript30k: Leveraging Multilingual Embeddings to Extend Cross Script Parallel Data}, 
      author={Christopher Driggers-Ellis and Detravious Brinkley and Ray Chen and Aashish Dhawan and Daisy Zhe Wang and Christan Grant},
      year={2025},
      eprint={2512.11074},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2512.11074}, 
}
@article{bai2025qwen25vl,
  title={Qwen2.5-VL Technical Report},
  author={Bai, Shuai and Chen, Keqin and Liu, Xuejing and Wang, Jialin and Ge, Wenbin and Song, Sibo and Dang, Kai and Wang, Peng and Wang, Shijie and Tang, Jun and others},
  journal={arXiv preprint arXiv:2502.13923},
  year={2025}
}
@article{bai2025qwen3vl,
  title={Qwen3-VL Technical Report},
  author={Bai, Shuai and Cai, Yuxuan and Chen, Ruizhe and Chen, Keqin and Chen, Xionghui and Cheng, Zesen and Deng, Lianghao and Ding, Wei and Gao, Chang and Ge, Chunjiang and others},
  journal={arXiv preprint arXiv:2511.21631},
  year={2025}
}
@inproceedings{dhawan-etal-2026-improving,
    title = "Improving Indigenous Language Machine Translation with Synthetic Data and Language-Specific Preprocessing",
    author = "Dhawan, Aashish  and
      Driggers-Ellis, Christopher  and
      Grant, Christan  and
      Wang, Daisy Zhe",
    editor = "Ojha, Atul Kr.  and
      Liu, Chao-hong  and
      Vylomova, Ekaterina  and
      Pirinen, Flammie  and
      Washington, Jonathan  and
      Oco, Nathaniel  and
      Zhao, Xiaobing",
    booktitle = "Proceedings for the Ninth Workshop on Technologies for Machine Translation of Low Resource Languages ({L}o{R}es{MT} 2026)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.loresmt-1.10/",
    doi = "10.18653/v1/2026.loresmt-1.10",
    pages = "119--126",
    ISBN = "979-8-89176-366-1",
    abstract = "Machine translation for Indigenous and other low-resource languages is constrained by limited parallel data, orthographic variation, and evaluation instability for morphologically rich languages. In this work, we study Spanish{--}Aymara, Spanish{--}Guarani, and Spanish{--}Quechua translation in the context of the AmericasNLP benchmarks, focusing on data-centric improvements rather than architectural changes. We augment curated parallel corpora with forward-translated synthetic sentence pairs generated using a high-capacity multilingual translation model, while applying conservative, language-specific preprocessing tailored to each language. Training data is filtered using length-ratio constraints and deduplication, whereas official development sets are left unfiltered to ensure fair evaluation. We fine-tune a multilingual mBART model under curated-only and curated+synthetic settings and evaluate performance primarily using chrF++, which is better suited for agglutinative languages than BLEU. Across all three languages, synthetic data augmentation consistently improves chrF++, with the largest gains observed for Aymara and Guarani, while Quechua benefits primarily from deterministic orthographic normalization. Our analysis highlights both the effectiveness and the limitations of generic preprocessing for highly agglutinative languages, suggesting that data-centric augmentation and language-aware normalization are strong, reproducible baselines for low-resource Indigenous language machine translation."
}
@inproceedings{tiedemann2012parallel,
  title={Parallel data, tools and interfaces in OPUS.},
  author={Tiedemann, J{\"o}rg},
  booktitle={Lrec},
  volume={2012},
  pages={2214--2218},
  year={2012}
}
@inproceedings{xiao-etal-2025-text,
    title = "From Text to Multi-Modal: Advancing Low-Resource-Language Translation through Synthetic Data Generation and Cross-Modal Alignments",
    author = "Xiao, Bushi  and
      Shen, Qian  and
      Wang, Daisy Zhe",
    editor = "Ojha, Atul Kr.  and
      Liu, Chao-hong  and
      Vylomova, Ekaterina  and
      Pirinen, Flammie  and
      Washington, Jonathan  and
      Oco, Nathaniel  and
      Zhao, Xiaobing",
    booktitle = "Proceedings of the Eighth Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2025)",
    month = may,
    year = "2025",
    address = "Albuquerque, New Mexico, U.S.A.",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.loresmt-1.4/",
    doi = "10.18653/v1/2025.loresmt-1.4",
    pages = "24--35",
    ISBN = "979-8-89176-230-5",
    abstract = "In this study, we propose a novel paradigm for multi-modal low resource language dataset generation that eliminates dependency on existing parallel multi-modal datasets. Leveraging advances in large image-generation models, we introduce a systematic pipeline that transforms text-only parallel corpora into rich multi-modal translation datasets. We then validate the generated content through human evaluation. We design and implement a new MMT model framework suitable for our new generated dataset. The model contains a verification mechanism with a large language model to ensure consistency between visual content and textual translations. Experimental results across four African low-resource languages with less than 10k training corpus demonstrate significant improvements over NLLB baselines, with average gains of up to 9.8{\%} in BLEU score and 4.3{\%} in METEOR score. Our method shows particular effectiveness in correctly translating concrete objects and contextual elements, suggesting its potential for improving low-resource machine translation through visual grounding."
}
@inproceedings{popovic-2015-chrf,
  title     = {chr{F}: character n-gram {F}-score for automatic {MT} evaluation},
  author    = {Popovi{\'c}, Maja},
  booktitle = {Proceedings of the Tenth Workshop on Statistical Machine Translation},
  year      = {2015},
  publisher = {Association for Computational Linguistics},
  url       = {https://aclanthology.org/W15-3049/},
  doi       = {10.18653/v1/W15-3049}
}

@inproceedings{popovic-2017-chrf,
  title     = {chr{F}++: words helping character n-grams},
  author    = {Popovi{\'c}, Maja},
  booktitle = {Proceedings of the Second Conference on Machine Translation},
  year      = {2017},
  publisher = {Association for Computational Linguistics},
  url       = {https://aclanthology.org/W17-4770/},
  doi       = {10.18653/v1/W17-4770}
}
  title={No language left behind: Scaling human-centered machine translation},
  author={Costa-Juss{\`a}, Marta R and Cross, James and {\c{C}}elebi, Onur and Elbayad, Maha and Heafield, Kenneth and Heffernan, Kevin and Kalbassi, Elahe and Lam, Janice and Licht, Daniel and Maillard, Jean and others},
  journal={arXiv preprint arXiv:2207.04672},
  year={2022}
}
@inproceedings{ebrahimi-etal-2023-findings,
    title = "Findings of the {A}mericas{NLP} 2023 Shared Task on Machine Translation into Indigenous Languages",
    author = "Ebrahimi, Abteen  and
      Mager, Manuel  and
      Rijhwani, Shruti  and
      Rice, Enora  and
      Oncevay, Arturo  and
      Baltazar, Claudia  and
      Cort{\'e}s, Mar{\'i}a  and
      Monta{\~n}o, Cynthia  and
      Ortega, John E.  and
      Coto-solano, Rolando  and
      Cruz, Hilaria  and
      Palmer, Alexis  and
      Kann, Katharina",
    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-1.23/",
    doi = "10.18653/v1/2023.americasnlp-1.23",
    pages = "206--219",
    abstract = "In this work, we present the results of the AmericasNLP 2023 Shared Task on Machine Translation into Indigenous Languages of the Americas. This edition of the shared task featured eleven language pairs, one of which {--} Chatino-Spanish {--} uses a newly collected evaluation dataset, consisting of professionally translated text from the legal domain. Seven teams participated in the shared task, with a total of 181 submissions. Additionally, we conduct a human evaluation of the best system outputs, and compare them to the best submissions from the prior shared task. We find that this analysis agrees with the quantitative measures used to rank submissions, which shows further improvements of 9.64 ChrF on average across all languages, when compared to the prior winning system."
}

@article{robertson2009probabilistic,
  author = {Robertson, Stephen and Zaragoza, Hugo},
  title = {The Probabilistic Relevance Framework: BM25 and Beyond},
  year = {2009},
  publisher = {Now Publishers Inc.},
  address = {Hanover, MA, USA},
  volume = {3},
  number = {4},
  issn = {1554-0669},
  doi = {10.1561/1500000019},
  journal = {Foundations and Trends in Information Retrieval},
  month = apr,
  pages = {333--389},
  numpages = {57}
}

@article{comanici2025gemini25,
  title={Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities},
  author={Comanici, Gheorghe and Bieber, Eric and Schaekermann, Mike and Pasupat, Ice and Sachdeva, Noveen and Dhillon, Inderjit and Blistein, Marcel and Ram, Ori and Zhang, Dan and Rosen, Evan and others},
  journal={arXiv preprint arXiv:2507.06261},
  year={2025}
}

@article{openai2024gpt4technicalreport,
  title={GPT-4 Technical Report},
  author={{OpenAI} and Achiam, Josh and Adler, Steven and Agarwal, Sandhini and Ahmad, Lama and Akkaya, Ilge and Aleman, Florencia Leoni and Almeida, Diogo and Altenschmidt, Janko and Altman, Sam and others},
  journal={arXiv preprint arXiv:2303.08774},
  year={2024}
}

@misc{qwen3technicalreport,
      title={Qwen3 Technical Report}, 
      author={{Qwen Team}},
      year={2025},
      eprint={2505.09388},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2505.09388}, 
}

@inproceedings{coto-solano-2021-explicit,
    title = "Explicit Tone Transcription Improves {ASR} Performance in Extremely Low-Resource Languages: A Case Study in {B}ribri",
    author = "Coto-Solano, Rolando",
    editor = "Mager, Manuel  and
      Oncevay, Arturo  and
      Rios, Annette  and
      Ruiz, Ivan Vladimir Meza  and
      Palmer, Alexis  and
      Neubig, Graham  and
      Kann, Katharina",
    booktitle = "Proceedings of the First Workshop on Natural Language Processing for Indigenous Languages of the Americas",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.americasnlp-1.20/",
    doi = "10.18653/v1/2021.americasnlp-1.20",
    pages = "173--184",
    abstract = "Linguistic tone is transcribed for input into ASR systems in numerous ways. This paper shows a systematic test of several transcription styles, using as an example the Chibchan language Bribri, an extremely low-resource language from Costa Rica. The most successful models separate the tone from the vowel, so that the ASR algorithms learn tone patterns independently. These models showed improvements ranging from 4{\%} to 25{\%} in character error rate (CER), and between 3{\%} and 23{\%} in word error rate (WER). This is true for both traditional GMM/HMM and end-to-end CTC algorithms. This paper also presents the first attempt to train ASR models for Bribri. The best performing models had a CER of 33{\%} and a WER of 50{\%}. Despite the disadvantage of using hand-engineered representations, these models were trained on only 68 minutes of data, and therefore show the potential of ASR to generate further training materials and aid in the documentation and revitalization of the language."
}

@inproceedings{bui-etal-2026-findings,
title = "Findings of the {A}mericas{NLP} 2026 Shared Task on Cultural Image Captioning for {I}ndigenous Languages",
author = {Bui, Minh Duc and
Guzm{\'a}n, David and
Ebrahimi, Abteen and
Morales, Franklin and
Ag{\"u}ero-Torales, Marvin and
Insfrán, Raquel and
González, Cecilia and
Araujo, Ramón and
Cernuzzi, Luca and
Noh Chi, Carlos Raul and
Tec Cahun, Carlos Eduardo and
Poot Cohuo, Sindi Estrella and
Ben{\'i}tez Chi, Daniel Ricardo and
Palomo Ar{\'e}valo, Santos Natanael and
Canul Canche, Jessica Elizabeth and
Poot Poot, Deysi Aracely and
Dzib Dzib, Wendy Marleny and
Ake Pool, Eduardo Jos{\'e} and
Couoh Martin, Reynaldo Alexander and
Fernandez Sabido, Silvia and
Santiago Melchor, Luis Samuel and
Silverio, Sotero and
Pugh, Robert and
V{\'a}zquez, Ra{\'u}l and
Ortega, John E. and
Oncevay, Arturo and
Manrique, Rub{\'e}n and
Chiruzzo, Luis and
Coto-Solano, Rolando and
Mager, Elisabeth and
Rijhwani, Shruti and
Adelani, David Ifeoluwa and
Mager, Manuel and
von der Wense, Katharina},
booktitle = "Proceedings of the Sixth Workshop on NLP for Indigenous Languages of the Americas (AmericasNLP)",
month = jul,
year = "2026",
address = "San Diego, California",
publisher = "Association for Computational Linguistics",
}

