Awajun- OP : Multi-domain dataset for S panish–Awajun Machine Translation (2024)

Abstract: We introduce a Spanish-Awajun parallel dataset of 22k high-quality sentence pairs with the help of the journalistic organization Company C. This dataset consists of parallel data obtained from various web sources such as poems, stories, laws, protocols, guidelines, handbooks, the Bible, and news published by Company C. The study also includes an analysis of the dataset’s performance for Spanish-Awajun translation using a Transformer architecture with transfer learning from a parent model, utilizing Spanish-English and Spanish-Finnish as high-resource language-pairs. As far as we know, this is the first Spanish-Awajun machine translation study, and we hope that this work will serve as a starting point for future research on this neglected Peruvian language.

## introduction in the diverse linguistic landscape of the americas, peru boasts 47 native languages, including 4 andean and 43 amazonic languages @xcite . castilian spanish, the primary official language spoken by the majority, starkly contrasts with these native languages. this vast language diversity is both a cultural treasure and a significant communication barrier. although human translators have played a crucial role in bridging these gaps, their availability remains limited. peru recognizes the pivotal role of translation in politics, striving for equal language rights through initiatives like the national registry of interpreters and translators of indigenous languages @xcite . efforts to preserve peru's languages have been insufficient, with most endangered and lacking documentation @xcite . mainly passed 1 "ojo público" is a peruvian media outlet. it is operated by a non-profit journalistic organization based in lima down orally, these languages pose significant computational challenges for machine translation due to scarce monolingual or parallel texts. however, recent research has shown promise in neural machine translation (nmt) for select peruvian native languages like quechua ayacucho (quy), quechua cuzco (quz), aymara (aym), shipibo-konibo (shp), and asháninka (cni). despite progress, the awajun language (agr), with around 55,000 speakers, remains overlooked, lacking dedicated nmt research. furthermore, awajun's data in opus is limited to fewer than 7,000 sentences. given this context, exploring alternative approaches is crucial to developing more effective machine translation systems for awajun and creating new parallel corpora to support these efforts. in this work, we aim to provide a comprehensive introduction to the awajun language (see appendix b), introduce a new parallel corpus for spanish-awajun (see §3), and experiment with transfer learning strategies for developing, as far as we know, the first nmt systems for the language pair spanish-awajun (see §5 for nmt experiments and appendix a for related work). ## quispe chequea ojo público has developed a digital tool using artificial intelligence to produce journalistic verification content in multiple formats and up to three native languages of peru: quechua, aymara, and awajún. this platform automates text generation, translation, and conversion into audio messages, which can be broadcasted by radios in nine regions, including loreto, junín, amazonas, piura, san martín, ayacucho, apurímac, puno, and tacna. developed by a team of journalists, technologists, translators, and interpreters, the project aims to combat misinformation affecting citizens and communities in the andes and the amazon of perú. this study focuses on the translation component of the project, specifically from data compilation to training an nmt model from spanish to awajun. ## corpus development we first compared the opus dataset (currently available data) and the new corpora extracted that we call awajun-op. an official translator validated all sources for the corpora to ensure the same dialect is used and to verify the translation quality. ## datasets of high resource languages spanish-english dataset for pre-training, we used the europarl dataset for spanish-english (1.9m sentences) @xcite and for validation and testing the wmt2007 dataset @xcite . spanish-finnish dataset for pre-training, we used europarl (1.9m sentences) @xcite , eubookshop (1.8m) @xcite , and ted2020 (44k) @xcite datasets for spanish-finnish, this excluding 3k sentences for validation, and for testing, the tatoeba (9.9k) dataset @xcite . ## results and discussion it is also noted that employing awajun-op yielded a notable enhancement compared to the baseline, achieving an improvement in bleu score of +2.98 and a +8.52 in chrf. furthermore, utilizing spanish-finnish as the parent model resulted in a 0.28 increase in bleu and 0.92 in chrf for the test input (es) publicó el video original en su sitio web con el titular input (en) he posted the original video on his website with the headline reference (awajun) nagkamchaku video jiikbauwa nuna niina webjin titularan aputus jiikiu output video nagkamchaku jiikbauwa duka sitio webnum agagtmitkau in addition to the transfer learning experiment, we trained a gpt-babbage model. however, the results were unsatisfactory, and we decided to stop training this type of model. morevoer, we tested madlad-400 @xcite , which contains part of the ebible data for awajun, but it underperformed as well. bleu scores only may not appear promising, which is similar to the results for other lowresource languages from the americas. to complement the evaluation, a professional awajun translator assessed a sample of the outputs of the best model. ## conclusion in this study, we extracted and created new parallel corpora for spanish-awajun, which comes from different sources, such as stories, laws, protocols, or guidelines from the web, plus in-house translated news texts. this helped us to develop the first nmt models for spanish-awajun. our work revealed that implementing transfer learning with spanish-finnish as a parent language resulted in better outcomes for both the baseline and awajun-op. furthermore, we sought the assistance of a professional translator to validate our findings and obtain a human perspective on the quality of our model. despite the limited availability of data, our research produced promising results. we have taken the initial steps towards developing reliable translations in awajun. for future work, we aim to acquire additional monolingual data for back-translation and fine-tune large multilingual models such as nllb @xcite , among others.