# Automatic Translation between Mixtec to Spanish Languages Using Neural Networks
## 3 Related Work
### Combination of Corpora  
Table 3 presents studies related to machine translation using combined corpora. [PERSON] et al. [26] showed translation leveraging POS tagging in Thai, Myanmar, and English languages. They utilized BLEU, ChrF, and WER as evaluation metrics. [PERSON] et al. [27] explored transfer translation using transformer networks with a shared, subword-tokenized vocabulary, focusing on English and Khasi languages. The evaluation employed BLEU, precision, recall, and F1 metrics. [PERSON] et al. [28] proposed a multimodal translation approach using neural networks. Their corpus included images with titles translated from English to Hindi, evaluated using BLEU and ChrF metrics. Meanwhile, [PERSON] et al. [29] emphasized the use of a shared lexicon and back-translation, introducing transfer translation with lexicon embedding pretraining for Azerbaijan and Uzbek. BLEU was the chosen evaluation metric. [PERSON] et al. [30] presented a hybrid translation method that integrated bilingual dictionaries, grammatical corpus, and syntactic rules between Sanskrit and Hindi. They evaluated their approach using the BLEU metric. [PERSON] [31] discussed developing North Sami-German translation utilizing morphosyntactic parsers and rules, with WER as the evaluation metric. [PERSON] et al. [32] described a translation process through transliteration, transforming a high-resource language corpus into a low-resource language. This approach involved training transliteration models with Wikipedia texts and generating a parallel corpus through back-translation, using BLEU, BEER, and ChrF for evaluation. [PERSON] et al. [33] introduced a novel approach for neural translation in low-resource lan  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**References** & **Translation Techniques** & **Tools** & **Metrics** \\ \hline
[14] & SMT & Giza++, [PERSON] & WER 38, TER 0.86 \\
[15] & Transformer & [PERSON] & BLEU 5 \\
[16] & Transformer & [PERSON] & BLEU 5.67, ChrF 39.9 \\
[17] & Transformer, SMT & [PERSON], OpenNMT & BLEU 23.47 \\
[18] & Transformer & [PERSON] & BLEU 38.2, ChrF 16.2 \\
[19] & Transformer, SMT & [PERSON] & BLEU 33.0, BEER 0.576, \\  & & & characTER 0.705 \\
[22] & Transformer & Not specified & BLEU 37.5 \\
[23] & Transformer, SMT & [PERSON], [PERSON] & BLEU 21.9, ChrF 0.484 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of various translation studies utilizing parallel corpora within Statistical Machine Translation (SMT) and Transformer models. It details the references of the studies, the translation techniques employed, the specific tools used for the translation, and the metrics applied to evaluate translation accuracy, such as Word Error Rate (WER), BLEU score, and character error rate (ChrF).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**References** & **Translation Techniques** & **Tools** & **Metrics** \\ \hline
[24] & Transformer, SMT & [PERSON], [PERSON] & BLEU 32.94, ChrF 0.59, \\  & & & TER 0.656 \\
[25] & Transformer & OpenNMT & AAS 14.73, MAS 14.24, \\  & & & BLEU 14.70 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation of translation approaches using pseudo-parallel corpora.  
guages like Ethiopian, employing parallel texts and the BLEU metric for evaluation. Finally, [PERSON] et al. [34] reported on improving translation quality in low-resource settings for English-Manipuri languages through semi-supervised neural machine translation, using BLEU and ChrF as metrics.