Crosslingual Dependency Parsing of H awaiian and C ook I slands M āori using U niversal D ependencies (2025)

Abstract: This paper presents the first Universal Dependency (UD) treebank for ʻŌlelo Hawaiʻi (Hawaiian). We discuss some of the difficulties in describing Hawaiian grammar using UD, and train models for automatic parsing. We also combined this data with UD parses from another Eastern Polynesian language, Cook Islands Māori, to train a crosslingual Polynesian parser using UDPipe2. The crosslingual parser produced a statistically significant improvement of 2.4% in the labeled attachment score (LAS) when parsing Hawaiian, and this improvement didn’t produce a negative impact in the LAS of Cook Islands Māori. We will use this parser to accelerate the linguistic documentation of Hawaiian.

## introduction this paper presents the first attempt to construct a universal dependencies @xcite treebank for ' ōlelo hawai'i (hereafter: hawaiian). hawaiian is an indigenous polynesian language spoken in hawai'i as a community language by kānaka maoli (native hawaiians) and non-hawaiian residents @xcite . hawaiian has been the subject of intense revitalization efforts since the hawaiian cultural renaissance of the 1970s @xcite . given the need for increased grammatical analysis to further revitalization goals, nlp tools like parsing can potentially facilitate the creation of annotated corpora. here we describe the structure of the treebank and use the treebank of a related polynesian language (cook islands māori) to build a crosslingual polynesian ud parser. ## methodology we will first describe the structure of the treebanks, and then describe the experiments to parse the hawaiian and cim data using zero-shot, monolingual and crosslingual methods. in these experiments we attempt to leverage the similarities between hawaiian and cim to improve the performance of the hawaiian model: the model with the least amount of data. we also attempt to leverage a high-resource language, english, to investigate whether its models can aid in the initial parsing of these two low-resource languages. ## results ## discussions in this section, we discuss the performance of the monolingual and crosslingual models, the kinds of errors they make when parsing, and consider some issues encountered while constructing the hawaiian treebank. ## conclusions and future work in this paper, we presented a treebank for the eastern polynesian language hawaiian, and used this dataset, along with a treebank in cook islands māori, to construct a crosslingual model to parse both languages. the crosslingual model produced a statistically significant increase in performance for hawaiian in comparison to the monolingual model. these gains in hawaiian neither helped nor hurt the performance on cim. the zero-shot approach using an unrelated language (english) did not result in any remarkable increase to model performance. a zero-shot model of a related language might still be required to get initial parses when constructing new treebanks from scratch. much future work remains for this project. as for the models, we need to perform fine-tuning tests on llms (e.g. bert) and test if they will provide improvements in performance over udpipe2. labeling using llm prompting is more complex, as it touches upon issues of data sovereignty, and the transmission and potential use of this information by the companies that host the llms. as for the treebanks themselves, both of them need to be tagged for their morphological features (ufeats), and expanded so that they can dependably label larger collections of data. the hawaiian language has thousands of pages of text, especially in historical newspaper collections @xcite , available for parsing; similarly, there is a wealth of legacy information available for cook islands māori that could be analyzed with these parsers. evidently the hawaiian treebank is still extremely small, and the models here will be used for bootstrapping and expanding the treebank. in the work with cim, we used the @xcite model to obtain preliminary parses, correct them, and include these newly parsed sentences in the treebank. this approach has been very fruitful in expanding the cim dataset, from 126 sentences in @xcite to the present 663. we intend to use this approach for hawaiian as well, and use these new crosslingual models to accelerate the construction of the hawaiian treebank even further. going forth, we intend to coordinate the efforts of the hawaiian and cim teams during the annotation, so that any corrections due to improved understanding of linguistic structures can find their way into both sets. we also intend to expand the domains from which sentences come from to include more varieties of data (e.g. spoken sentences typical of transcribed interviews), so that we can ultimately release these models to the interested stakeholders in their respective communities. at present, our priority for the hawaiian parser is to facilitate its access and use by community scholars and organizations involved in language revitalization and pedagogical work. heeding present intra-community concerns about data stewardship @xcite , we hope to arrive at a wider consensus among stakeholders before releasing the hawaiian model for wider distribution and use. as for the cim, sharing the treebank and the model publicly presents similar concerns, and more consensus is needed before its release. this work is part of a larger project to train linguists and nlp specialists in the cook islands, who can collaborate with other scholars from polynesia in the documentation of their languages. we hope that this work will be used not only to tag collections in hawaiian and cim, but also to foster work in nlp in other polynesian and indigenous languages, accelerating documentation work to contribute to language revitalization, normalization, and reclamation efforts in the pacific and worldwide.