A New Benchmark for K alaallisut- D anish Neural Machine Translation (2024)

Abstract: Kalaallisut, also known as (West) Greenlandic, poses a number of unique challenges to contemporary natural language processing (NLP). In particular, the language has historically lacked benchmarking datasets and robust evaluation of specific NLP tasks, such as neural machine translation (NMT). In this paper, we present a new benchmark dataset for Greenlandic to Danish NMT comprising over 1.2m words of Greenlandic and 2.1m words of parallel Danish translations. We provide initial metrics for models trained on this dataset and conclude by suggesting how these findings can be taken forward to other NLP tasks for the Greenlandic language.

## introduction greenlandic (kalaallisut) is an inuit-yupik-unangan language spoken by around 60,000 people in greenland and denmark. while greenlandic is classified as 'vulnerable' according to the endangered languages project. 1 , it is nevertheless relatively healthy. local governance, media, and schooling up to university level are conducted either purely in greenlandic, the sole official language since 2009, or in both greenlandic and danish, the colonial language @xcite . greenlandic has a number of comparatively rare linguistic features, not least of which is the prominent use of polysynthesis @xcite . morphological complexity not only manifests in wordfinal inflections (mood, person, number, and so on) but also the theoretically indefinite productivity of adding morphemes to stems and concatenating other morphemes. for example, palasi means 'priest', palasi-nngor-poq means 'he becomes a priest', palasi-nngor-tip-paa means 'he causes him to become a priest', and palasi-nngor-tit-si-neq means 'the act of causing someone to become a priest'. ## conclusions this paper is a preliminary step towards training neural language technology for greenlandic and, crucially, empirically testing both the possibilities and limitations of this approach. we present a benchmark dataset for greenlandic-danish nmt as well as providing initial metrics from simple models trained on this dataset. these initial experiments are not intended to provide complete, industry-strength machine translation for greenlandic to danish. improvements in the area of greenlandic nmt and nlp more generally requires greater emphasis on the curation and stewardship of high quality training data. we believe that this focus would contribute greatly to greenland's already rich linguistic cultural heritage.