Mixtec–Spanish Parallel Text Dataset for Language Technology Development

Hermilo Santiago-Benito, Diana-Margarita Córdova-Esparza (), Juan Terven, Noé-Alejandro Castro-Sánchez, Teresa García-Ramirez, Julio-Alejandro Romero-González and José M. Álvarez-Alvarado
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Hermilo Santiago-Benito: Facultad de Informática, Universidad Autónoma de Querétaro, Av. de las Ciencias S/N, Campus Juriquilla, Querétaro 76230, Mexico
Diana-Margarita Córdova-Esparza: Facultad de Informática, Universidad Autónoma de Querétaro, Av. de las Ciencias S/N, Campus Juriquilla, Querétaro 76230, Mexico
Juan Terven: Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada—Unidad Querétaro, Instituto Politécnico Nacional, Cerro Blanco No. 141, Col. Colinas del Cimatario, Querétaro 76090, Mexico
Noé-Alejandro Castro-Sánchez: Centro Nacional de Investigación y Desarrollo Tecnológico, Tecnológico Nacional de México, Interior Internado Palmira S/N, Palmira, Cuernavaca 62493, Mexico
Teresa García-Ramirez: Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada—Unidad Querétaro, Instituto Politécnico Nacional, Cerro Blanco No. 141, Col. Colinas del Cimatario, Querétaro 76090, Mexico
Julio-Alejandro Romero-González: Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada—Unidad Querétaro, Instituto Politécnico Nacional, Cerro Blanco No. 141, Col. Colinas del Cimatario, Querétaro 76090, Mexico
José M. Álvarez-Alvarado: Facultad de Ingeniería, Universidad Autónoma de Querétaro, Querétaro 76010, Mexico

Data, 2025, vol. 10, issue 7, 1-15

Abstract: This article introduces a freely available Spanish–Mixtec parallel corpus designed to foster natural language processing (NLP) development for an indigenous language that remains digitally low-resourced. The dataset, comprising 14,587 sentence pairs, covers Mixtec variants from Guerrero (Tlacoachistlahuaca, Northern Guerrero, and Xochapa) and Oaxaca (Western Coast, Southern Lowland, Santa María Yosoyúa, Central, Lower Cañada, Western Central, San Antonio Huitepec, Upper Western, and Southwestern Central). Texts are classified into four main domains as follows: education, law, health, and religion. To compile these data, we conducted a two-phase collection process as follows: first, an online search of government portals, religious organizations, and Mixtec language blogs; and second, an on-site retrieval of physical texts from the library of the Autonomous University of Querétaro. Scanning and optical character recognition were then performed to digitize physical materials, followed by manual correction to fix character misreadings and remove duplicates or irrelevant segments. We conducted a preliminary evaluation of the collected data to validate its usability in automatic translation systems. From Spanish to Mixtec, a fine-tuned GPT-4o-mini model yielded a BLEU score of 0.22 and a TER score of 122.86, while two fine-tuned open source models mBART-50 and M2M-100 yielded BLEU scores of 4.2 and 2.63 and TER scores of 98.99 and 104.87, respectively. All code demonstrating data usage, along with the final corpus itself, is publicly accessible via GitHub and Figshare. We anticipate that this resource will enable further research into machine translation, speech recognition, and other NLP applications while contributing to the broader goal of preserving and revitalizing the Mixtec language.

Keywords: Mixtec language; parallel corpus; low resource language; OCR (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2025
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