# Automatic Translation between Mixtec to Spanish Languages Using Neural Networks  
[PERSON]  
1 Facultad de Informatica, Universidad Autonoma de Queretaro, Av. de las Ciencias S/N, Queretaro 76230, Mexico; [EMAIL_ADDRESS] (T.G.-R.); [EMAIL_ADDRESS] (J.-A.R.-G.)  
[PERSON]  
1 Facultad de Informatica, Universidad Autonoma de Queretaro, Av. de las Ciencias S/N, Queretaro 76230, Mexico; [EMAIL_ADDRESS] (T.G.-R.); [EMAIL_ADDRESS] (J.-A.R.-G.)  
[PERSON]  
2 Centro Nacional de Investigacion y Desarrollo Tecnologico Nacional, LicAccording to the catalog of Mexico's National Institute of Indigenous Languages (INALI) [7], there are 68 linguistic groups and 11 linguistic families. Linguistic groups form a set of linguistic variants related to an indigenous community. A linguistic family comprises languages that share a similar structure and lexicon, as well as a common historical origin. Mixtec belongs to the Oto-Manguean family, sharing characteristics with languages such as Triqui, Mixe-Zoque, Seri, Pame, Tlapaneco, Zapotec, and Mazahua, among others.  
As per the census by the National Institute of Statistics and Geography (INEGI) [8], in Mexico, 7,364,645 people aged three years or older speak an indigenous language, which is equivalent to 6% of the population. The languages with the highest number of speakers are Nahuatl, Maya, Tseltal, Tsotsil, and Mixtec. Mixtec has a total of 526,593 speakers and is the fifth most spoken language in Mexico. In accordance with the National Institute of Indigenous Peoples (INPI) [9], Mixtec communities are located in the states of Oaxaca, Puebla, and Guerrero.  
As per the catalog of (INALI) [7], the Mixtec language has 81 variants in Mexico. There could be even more variants, as each town may have its own Mixtec variant.  
In this work, we address the translation of texts from the Mixtec variants of the states of Guerrero and Oaxaca since the majority of the texts in the collected corpus are from these variants.  
The proposed methodology is applicable to any pair of languages with low digital resources. However, in this project, we focused on Mixtec-Spanish.  
A survey on the topic points out that there are no scientific publications regarding Mixtec-Spanish translation, causing a need for more methodology for Mixtec-Spanish translation. The objectives of this study are as follows:  
* Propose a methodology for Mixtec-Spanish language translation. 1. Corpus compilation. We performed the search for parallel corpora, manually corrected, and generated synthetic data. 2. Corpus preprocessing. This step involved cleaning special characters, numbers, duplicated sentences, and empty sentences. 3. Semi-automatic alignment. The texts are aligned semi-automatically to generate paired parallel corpora at the sentence level. 4. Translation training. We trained recurrent neural networks, bidirectional recurrent neural networks (BRNNs), and Transformers.
* Generate translation memories.
* Create a tokenized corpus at the word level.
* Generate synthetic Mixtec data with GPT-3.5.
* Evaluate the translation using the BLEU and TER metrics.
* Identify challenges in Mixtec-Spanish translation.  
This work focuses on the translation of educational and religious texts, as these constitute the majority of our current corpus domain. Our proposal represents the first methodology for Mixtec-Spanish translation in Mexico.  
This paper is structured as follows. Section 1 presents the introduction. Section 2 outlines the theoretical foundations of neural networks. Section 3 describes relevant works regarding translation. Section 4 describes our methodology for Mixtec-Spanish translation. Section 5 presents the results, followed by a discussion and conclusion in Sections 6 and 7.