Neural Machine Translation through Active Learning on low-resource languages: The case of S panish to M apudungun (2023)

Abstract: Active learning is an algorithmic approach that strategically selects a subset of examples for labeling, with the goal of reducing workload and required resources. Previous research has applied active learning to Neural Machine Translation (NMT) for high-resource or well-represented languages, achieving significant reductions in manual labor. In this study, we explore the application of active learning for NMT in the context of Mapudungun, a low-resource language spoken by the Mapuche community in South America. Mapudungun was chosen due to the limited number of fluent speakers and the pressing need to provide access to content predominantly available in widely represented languages. We assess both model-dependent and model-agnostic active learning strategies for NMT between Spanish and Mapudungun in both directions, demonstrating that we can achieve over 40% reduction in manual translation workload in both cases.

## introduction over the course of history, south america has been home to numerous indigenous cultures and languages @xcite , reflecting the region's rich linguistic diversity and heritage. unfortunately, the dominance of the spanish language in this region has threatened many indigenous languages, often leading to their decline or even extinction. this has resulted in an immeasurable cultural and historical loss for humanity, as language diversity vanishes @xcite . among the last remaining native languages is mapudungun, spoken in chile and argentina by nearly 1.8 million people (mapuches), but only 10% of them handle the language correctly and barely another 10% understand it. in the same spirit, the conadi indigenous languages program foot_0 predicts that this language will become extinct in a few generations, mainly due to the lack of individuals that can speak this language. despite this, there are still groups within chile that only speak mapudungun, leaving them sometimes excluded from the rest of society. furthermore, the social tension over the past few years has raised native indigenous people to the forefront of discussion, attracting high interest in the community to find ways to include them in society as equals. unfortunately, the availability of human translators fluent in those languages is minimal, and no automated translators exist today supporting those languages. in this work, we present an active learning setting to improve the efficiency and efficacy of machine translation for low-resource languages, in this case, mapudungun. in other words, we aim to reduce the effort made by human translators given that the quantity of people fluent in mapudungun is scarce. given this, the task of translating and reviewing large amounts of text is unattainable. one of the main tasks of active learning is choosing the appropriate data points (texts) to be translated by human translators to train a neural machine translation (nmt) model with as few examples as possible. to evaluate our approach, we utilized an open-source corpus from the avenue project @xcite and supplemented it by scraping the web for spanish-mapudungun sentence pairs. we assembled a dataset of approximately 30,000 pairs, creating a comprehensive corpus for our research. we simulate an offline active learning setting to measure the amount of work that can be reduced by using different active learning strategies. the main contributions of this paper are: (1) proposing active learning training strategies to reduce low-resource language speaker translators workload by more than 40%, (2) finetuning a mapudungun nmt model capable of obtaining competitive results and (3) sharing our code for research reproducibility foot_1 . ## related work active learning active learning is an effective machine learning training approach where the algorithm actively selects informative data to learn from, resulting in improved performance with fewer labeled instances @xcite . while initially applied to text classification, information retrieval, classification, and regression tasks @xcite @xcite @xcite , active learning has recently been extended to tasks such as named entity recognition, text summarization, and machine translation @xcite @xcite . this study investigates unexplored potential of active learning in machine translation for untranslated examples in mapudungun, a low-resource language. ## methodology in this section we describe in detail the active learning framework proposed for nmt on low-resource languages and the type of active learning strategies depending if there is or not a machine learning model involved in the selection of examples for being labeled. in figure 1 , we show the active learning setting used in this work. in the first step, we initialize an nmt model, then given a monolingual corpus in spanish and an active learning strategy, it chooses examples for being translated by an oracle to mapudungun. after obtaining the translated sentences, we fine-tune the nmt model, update its parameters, and then use this updated version to select new sentences for labeling. we use four active learning strategies to select sentences for an oracle's translation: entropy sampling, margin sampling, confidence sampling, and decay logarithm frequency. the strategies chosen are pertinent to both spanish to mapudungun and mapudungun to spanish translations in low-resource scenarios. they address key issues such as uncertainty, data diversity, and model reliance, thus optimizing translation models and aiding language preservation. the strategy's reliance on the model varies; model-agnostic strategies don't need it for selecting sentences, while model-related ones use its certainty level. the number of active learning iterations and oracle translation requests is userdetermined at the start of training. ## conclusion in conclusion, this study revealed that delfy was the most effective active learning strategy for spanish to mapudungun translation, while margin sampling outperformed in mapudungun to spanish. in both cases, training with active learning strategies reduced workload by over 40%. our comparative analysis, driven by the diverse approaches of the chosen strategies, identifies the most efficient methods for low-resource translation tasks. this research is crucial for languages particularly mapudungun, as it fosters information access and reduces language barriers for indigenous communities. future work will focus on designing active learning strategies specifically for low-resource languages.