# Automatic Translation between Mixtec to Spanish Languages Using Neural Networks
## 4 Methodology
### Phase 2: Developing and Applying the Corpus Processing Module
* Tonal Complexity: The diversity of tonal variations in low-resource languages adds complexity to the application of natural language processing algorithms, including machine translation and grammatical tagging. * Inconsistency in Texts: A notable challenge is the inconsistency in texts within parallel corpora used for training. Such inconsistencies can adversely affect machine translation efficiency, as highlighted by [PERSON] et al. [40] and [PERSON] et al. [41] in their research on neural network-based normalization for low-resource language environments. \begin{table}
\begin{tabular}{c c} \hline \hline
**References** & **BLEU** \\ \hline [PERSON] et al. [15] & +5 \\ [PERSON] et al. [17] & 23.47 \\ [PERSON] et al. [18] & 16.2 \\ [PERSON] et al. [22] & 37.5 \\ [PERSON] et al. [23] & 26.8 \\ Our approach & 52.39 * \\ \hline \hline \end{tabular}

* Average BLEU score between BRNN, RNN, and Transformer.

\end{table} 
Table 15: Comparison with respect to related works in low-digital-resource languages. * Lack of keyboards: In low-resource languages, the vast majority of them do not have the characters defined in a keyboard that allows writing texts in these languages. * Lack of optical character recognition: The vast majority of languages with low digital resources are not incorporated into the language catalogs of the recognition tools, which allow identifying and correcting the characters in these languages. ## 7 Conclusions

In this study, we successfully developed the first methodology for Mixtec-Spanish and Spanish-Mixtec translation in Mexico. Our approach achieved a remarkable 28% improvement in BLEU scores compared to existing works. This enhancement was possible through the integration of neural network techniques, corpus processing, synthetic data generation, and back-translation. We found that preprocessing the corpus by normalizing it significantly enhanced translation accuracy and improved BLEU scores. During this phase, we encountered and overcame challenges with the transformer network in decoding Mixtec texts. We innovated an algorithm to effectively remove or replace unique characters in Mixtec syntax, which was problematic for translation. Furthermore, the use of synthetic data generation strategies significantly expanded our parallel corpus. The methodology we developed holds excellent promise for adapting text translations in other indigenous languages such as Amuzgo, Tlapaneco, and Zoque. We also created Mixtec-Spanish translation memories, which will be an asset for the creation of voice recognition, grammatical tagging, linguistic studies, multilingual translation involving other Mexican indigenous languages, automatic summary generation, speech-to-text conversion, sentiment analysis, and language detection. In future work, we plan to expand the parallel corpus and refine the results further by incorporating synthetic data generation with more advanced models such as GPT-4 [42], Gemini Ultra [43], or Claude 3 Opus [44], as well as adding other augmentation techniques such as noise injection or transfer learning from high-resource language models. Conceptualization, H.S.-B., D.-M.C.-E. and N.-A.C.-S.; methodology H.S.-B. and D.-M.C.-E.; software, H.S.-B. ; validation, H.S.-B., D.-M.C.-E. and N.-A.C.-S.; formal analysis, H.S.-B., D.-M.C.-E. and N.-A.C.-S.; investigation, H.S.-B., D.-M.C.-E. and N.-A.C.-S.; resources H.S.-B., D.-M.C.-E., N.-A.C.-S., J.-A.R.-G., T.G.-R. and J.T. ; writing--original draft preparation, H.S.-B., D.-M.C.-E. and N.-A.C.-S.; writing--review and editing, H.S.-B., D.-M.C.-E., N.-A.C.-S., J.-A.R.-G., T.G.-R. and J.T. ; visualization, H.S.-B., D.-M.C.-E., N.-A.C.-S., J.-A.R.-G., T.G.-R. and J.T. ; supervision, D.-M.C.-E. and N.-A.C.-S.; project administration, H.S.-B., D.-M.C.-E., N.-A.C.-S., J.-A.R.-G., T.G.-R. and J.T. All authors have read and agreed to the published version of the manuscript. ## References

* [PERSON] (2020) [PERSON] MTUOC: Easy and free integration of NMT systems in professional translation environments.