Tokenization on Trial: The Case of Kalaallisut – Danish Legal Machine Translation (2025)

Abstract: The strengths of subword tokenization have been widely demonstrated when applied to higher-resourced, morphologically simple languages. However, it is not self-evident that these results transfer to lower-resourced, morphologically complex languages. In this work, we investigate the influence of different subword segmentation techniques on machine translation between Danish and Kalaallisut, the official language of Greenland. We present the first semi-manually aligned parallel corpus for this language pair, and use it to compare subwords from unsupervised tokenizers and morphological segmenters. We find that Unigram-based segmentation both preserves morphological boundaries and handles out-of-vocabulary words adequately, but that this does not directly correspond to superior translation quality. We hope that our findings lay further groundwork for future efforts in neural machine translation for Kalaallisut.

## introduction in contrast to many of the world's indigenous languages facing challenges in revitalization as a result of colonialism @xcite , kalaallisut (west greenlandic) has a vibrant linguistic ecosystem. spoken as a first language by people of all ages @xcite , kalaallisut is used in all aspects of daily life by most of the population @xcite , from teenagers texting @xcite to everyday communication @xcite . it is also supported by language policies 1 https://github.com/esther2000/ tokenization-on-trial 1.0 1.5 2.0 2.5 3.0 3.5 4.0 ## a reliably parallel dataset aligning parallel datasets is non-trivial in the case of highly inflectional, low-resource languages. popular alignment methods require pre-trained language embeddings @xcite , pre-suppose tokenized text @xcite , or assume that sequence lengths correspond directly across languages @xcite . @xcite conducted extensive experiments on alignment of polysynthetic languages, but found that their result for danish-kalaallisut was too noisy and thus not useful downstream. their data was sourced from magazines, however. we hypothesize that choosing a more structured domain (e.g., legal) may make alignment more feasible. data collection oqaasileriffik referred us to the collection of parallel legal texts, hosted by the greenlandic government. 4 although kalaallisut is the most widely spoken language (united nations, 2023), greenland's legal system is bilingual, and laws and legal documents are often drafted in danish. in this work, we use the law collections, which is an archive of the legislation of greenland's self-government, danish legislation applicable to greenland, and international regulations that are relevant to greenland. 5 in total, it consists of 2,545 publicly available documents in html format, originally written between 1908 and 2024, many of which are manually translated. alignment and filtering through scraping, we retrieve parallel documents, filtering out any nontranslated documents. however, to obtain parallel sentences, we need to align the text. as mentioned, this assumes data or experimental consensus which is not available for kalaallisut (i.e., language embeddings and tokenized text). fortunately, legal text is highly structured: our scraped data contains strict paragraph markers (e.g., § 2) and clause enumerations (e.g., a)), equally across source and target. we leverage this structure by aligning through enumeration: for each document, we retrieve all enumerated text segments and align accordingly in case of 1:1 correspondence with enumeration markers. as an additional advantage, alignment on enumerated clauses furthermore serves as a filtering step. for example, lessstructured introductory texts and sensitive information such as email addresses and full names are automatically filtered out. we strip the enumeration token from each line, apply deduplication and subsequently extract 1,000 lines for the validation set, and 1,000 other lines for the test set. we use the remaining lines as the training set. importantly, we make the design choice to not remove near-duplicates. legal texts can be highly formulaic, and since we perform an in-domain evaluation which cannot be expected to be widely generalizable regardless (see: limitations), we decide to leave them in. ## experiments since we are interested in isolating the effects of subword segmentation on nmt performance, we train dedicated bilingual mt models from scratch. our experimental set-up consists of three steps: subword segmentation, machine translation, and evaluation, each described in more detail below. ## analysis to add more context to our findings, we perform additional analyses. ## discussion and recommendations given the lack of research for kalaallisut nmt, we posit that collaboration between nlp researchers, greenlandic language experts, and nonspecialist native speakers of the language is crucial. in addition to a parallel dataset and experimental documentation, we aim to contribute to nmt for kalaallisut by providing some high-level recommendations below. explore additional resources beyond the legal domain, kalaallisut boasts a wealth of traditional linguistic resources, like dictionaries @xcite and formal grammars @xcite @xcite @xcite . due to greenland's relationship with denmark, national newspapers and official government resources are often available in both kalaallisut and danish, which allows "pseudoparallel" corpora to be compiled through webcrawling @xcite . similar efforts could be applied to other domains. additional digital resources for kalaallisut include a spell-checker, text-to-speech system, and grapheme-to-phoneme converter (oqaasileriffik), a hand tagged corpus (per langgård and visl team), and recent nmt benchmark dataset @xcite . with the exception of the latter, no previous works make use of this wealth of resources, and thus practitioners may benefit from their inclusion going forward. consider other dialects even among lowresource languages, the majority of research attention is paid to standard language varieties, with the risk that non-standard dialects are left behind @xcite . this holds true for greenlandic, where works on non-standard dialects are far outnumbered by those for kalaallisut. the greenlandic language contains three main dialects: kalaallisut (the western dialect, and the standard form), tunumiisut (spoken in eastern greenland), and inuktun (used in the northern region). 10 while kalaallisut is predominant, all dialects are vital to understanding greenland's linguistic diversity. only a few grammar books are available for tunumiisut @xcite @xcite and inuktun @xcite , however. no nlp datasets have as yet been published, despite their apparent presence on social media. this suggests that the language's integration into advanced language technologies is still limited @xcite , and future works for greenlandic nlp could thus benefit from curation of resources and experimentation across dialects. ## conclusion in this paper, we build upon the current state of danish↔kalaallisut nmt research, noting a sparsity of benchmarks and open-science experimental groundwork. we then introduce a new semimanually aligned corpus of parallel legal texts for this language pair. leveraging this, we conduct systematic experiments on subword segmentation, analyzing the impact of both traditional subword tokenizers (bpe, unigram), and morphological segmentation (morfessor 2.0, flatcat) on downstream nmt performance. while segmentation techniques generally improve translation, we do not find one segmenter that beats the others in all aspects. ideally, more data and evidence are needed to draw more robust conclusions.