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In this paper, we challenge the ACL community to reckon with historical and ongoing colonialism by adopting a set of ethical obligations and best practices drawn from the Indigenous studies literature. While the vast majority of NLP research focuses on a very small number of very high resource languages (English, Chinese, etc), some work has begun to engage with Indigenous languages. No research involving Indigenous language data can be considered ethical without first acknowledging that Indigenous languages are not merely very low resource languages. The toxic legacy of colonialism permeates every aspect of interaction between Indigenous communities and outside researchers. To this end, we propose that the ACL draft and adopt an ethical framework for NLP researchers and computational linguists wishing to engage in research involving Indigenous languages. 

Akuzipik (Yupigestun/Yupik/St. Lawrence Island Yupik/Siberian Yupik/Chaplinski Yupik) is an endangered language belonging to the Yupik branch of the Inuit-Yupik-Unangan language family. It is currently spoken by 800-900 people in the Bering Strait region, mainly on St. Lawrence Island, Alaska (St. Lawrence Island Yupik), and on the coast of the Chukotka Peninsula, in Russia (Chaplinski Yupik) (de Reuse 1994; Schwartz et al. 2019). The linguistic differences between these two varieties seem to be minor and not affect mutual intelligibility (Krauss 1975). The language has been undergoing a rapid generational shift, beginning in the 1950s in Russia and in the 1990s in Alaska (Schwartz et al. 2019). 

This paper describes the development of the first Universal Dependencies (UD) treebank for St. Lawrence Island Yupik, an endangered language spoken in the Bering Strait region. While the UD guidelines provided a general framework for our annotations, language-specific decisions were made necessary by the rich morphology of the polysynthetic language. Most notably, we annotated a corpus at the morpheme level as well as the word level. The morpheme level annotation was conducted using an existing morphological analyzer and manual disambiguation. By comparing the two resulting annotation schemes, we argue that morpheme-level annotation is essential for polysynthetic languages like St. Lawrence Island Yupik. Word-level annotation results in degenerate trees for some Yupik sentences and often fails to capture syntactic relations that can be manifested at the morpheme level. Dependency parsing experiments provide further support for morpheme-level annotation. Implications for UD annotation of other polysynthetic languages are discussed. 

Abstract Prior studies in multilingual language modeling (e.g., Cotterell et al., 2018; Mielke et al., 2019) disagree on whether or not inflectional morphology makes languages harder to model. We attempt to resolve the disagreement and extend those studies. We compile a larger corpus of 145 Bible translations in 92 languages and a larger number of typological features.1 We fill in missing typological data for several languages and consider corpus-based measures of morphological complexity in addition to expert-produced typological features. We find that several morphological measures are significantly associated with higher surprisal when LSTM models are trained with BPE-segmented data. We also investigate linguistically motivated subword segmentation strategies like Morfessor and Finite-State Transducers (FSTs) and find that these segmentation strategies yield better performance and reduce the impact of a language’s morphology on language modeling. 

St. Lawrence Island Yupik (ISO 639-3: ess) is an endangered polysynthetic language in the Inuit-Yupik language family indigenous to Alaska and Chukotka. This work presents a step-by-step pipeline for the digitization of written texts, and the first publicly available digital corpus for St. Lawrence Island Yupik, created using that pipeline. This corpus has great potential for future linguistic inquiry and research in NLP. It was also developed for use in Yupik language education and revitalization, with a primary goal of enabling easy access to Yupik texts by educators and by members of the Yupik community. A secondary goal is to support development of language technology such as spell-checkers, text-completion systems, interactive e-books, and language learning apps for use by the Yupik community. 

St. Lawrence Island Yupik is an endangered polysynthetic language of the Bering Strait region. While conducting linguistic fieldwork between 2016 and 2019, we observed substantial support within the Yupik community for language revitalization and for resource development to support Yupik education. To that end, Chen & Schwartz (2018) implemented a finite-state morphological analyzer as a critical enabling technology for use in Yupik language education and technology. Chen & Schwartz (2018) reported a morphological analysis coverage rate of approximately 75% on a dataset of 60K Yupik tokens, leaving considerable room for improvement. In this work, we present a re-implementation of the Chen & Schwartz (2018) finite-state morphological analyzer for St. Lawrence Island Yupik that incorporates new linguistic insights; in particular, in this implementation we make use of the Paradigm Function Morphology (PFM) theory of morphology. We evaluate this new PFM-based morphological analyzer, and demonstrate that it consistently outperforms the existing analyzer of Chen & Schwartz (2018) with respect to accuracy and coverage rate across multiple datasets. 

Many techniques in modern computational linguistics and natural language processing (NLP) make the assumption that approaches that work well on English and other widely used European (and sometimes Asian) languages are “language agnostic” – that is that they will also work across the typologically diverse languages of the world. In high-resource languages, especially those that are analytic rather than synthetic, a common approach is to treat morphologically-distinct variants of a common root (such as dog and dogs) as completely independent word types. Doing so relies on two main assumptions: that there exist a limited number of morphological inflections for any given root, and that most or all of those variants will appear in a large enough corpus (conditioned on assumptions about domain, etc.) so that the model can adequately learn statistics about each variant. Approaches like stemming, lemmatization, morphological analysis, subword segmentation, or other normalization techniques are frequently used when either of those assumptions are likely to be violated, particularly in the case of synthetic languages like Czech and Russian that have more inflectional morphology than English. Within the NLP literature, agglutinative languages like Finnish and Turkish are commonly held up as extreme examples of morphological complexity that challenge common modelling assumptions. Yet, when considering all of the world’s languages, Finnish and Turkish are closer to the average case in terms of synthesis. When we consider polysynthetic languages (those at the extreme of morphological complexity), even approaches like stemming, lemmatization, or subword modelling may not suffice. These languages have very high numbers of hapax legomena (words appearing only once in a corpus), underscoring the need for appropriate morphological handling of words, without which there is no hope for a model to capture enough statistical information about those words. Moreover, many of these languages have only very small text corpora, substantially magnifying these challenges. To this end, we examine the current state-of-the-art in language modelling, machine translation, and predictive text completion in the context of four polysynthetic languages: Guaraní, St. Lawrence Island Yupik, Central Alaskan Yup’ik, and Inuktitut. We have a particular focus on Inuit-Yupik, a highly challenging family of endangered polysynthetic languages that ranges geographically from Greenland through northern Canada and Alaska to far eastern Russia. The languages in this family are extraordinarily challenging from a computational perspective, with pervasive use of derivational morphemes in addition to rich sets of inflectional suffixes and phonological challenges at morpheme boundaries. Finally, we propose a novel framework for language modelling that combines knowledge representations from finite-state morphological analyzers with Tensor Product Representations (Smolensky, 1990) in order to enable successful neural language models capable of handling the full linguistic variety of typologically variant languages. 

Many techniques in modern computational linguistics and natural language processing (NLP) make the assumption that approaches that work well on English and other widely used European (and sometimes Asian) languages are “language agnostic” – that is that they will also work across the typologically diverse languages of the world. In high-resource languages, especially those that are analytic rather than synthetic, a common approach is to treat morphologically-distinct variants of a common root (such as dog and dogs) as completely independent word types. Doing so relies on two main assumptions: that there exist a limited number of morphological inflections for any given root, and that most or all of those variants will appear in a large enough corpus (conditioned on assumptions about domain, etc.) so that the model can adequately learn statistics about each variant. Approaches like stemming, lemmatization, morphological analysis, subword segmentation, or other normalization techniques are frequently used when either of those assumptions are likely to be violated, particularly in the case of synthetic languages like Czech and Russian that have more inflectional morphology than English. Within the NLP literature, agglutinative languages like Finnish and Turkish are commonly held up as extreme examples of morphological complexity that challenge common modelling assumptions. Yet, when considering all of the world’s languages, Finnish and Turkish are closer to the average case in terms of synthesis. When we consider polysynthetic languages (those at the extreme of morphological complexity), even approaches like stemming, lemmatization, or subword modelling may not suffice. These languages have very high numbers of hapax legomena (words appearing only once in a corpus), underscoring the need for appropriate morphological handling of words, without which there is no hope for a model to capture enough statistical information about those words. Moreover, many of these languages have only very small text corpora, substantially magnifying these challenges. To this end, we examine the current state-of-the-art in language modelling, machine translation, and predictive text completion in the context of four polysynthetic languages: Guaraní, St. Lawrence Island Yupik, Central Alaskan Yup’ik, and Inuktitut. We have a particular focus on Inuit-Yupik, a highly challenging family of endangered polysynthetic languages that ranges geographically from Greenland through northern Canada and Alaska to far eastern Russia. The languages in this family are extraordinarily challenging from a computational perspective, with pervasive use of derivational morphemes in addition to rich sets of inflectional suffixes and phonological challenges at morpheme boundaries. Finally, we propose a novel framework for language modelling that combines knowledge representations from finite-state morphological analyzers with Tensor Product Representations (Smolensky, 1990) in order to enable successful neural language models capable of handling the full linguistic variety of typologically variant languages. 

St. Lawrence Island Yupik is an endangered language of the Bering Strait region. In this paper, we describe our work on Yupik jointly leveraging computational morphology and linguistic fieldwork, outlining the multilayer virtuous cycle that we continue to refine in our work to document and build tools for the language. After developing a preliminary morphological analyzer from an existing pedagogical grammar of Yupik, we used it to help analyze new word forms gathered through fieldwork. While in the eld, we augmented the analyzer to include in- sights into the lexicon, phonology, and morphology of the language as they were gained during elicitation sessions and subsequent data analysis. The analyzer and other tools we have developed are improved by a corpus that continues to grow through our digitization and documentation efforts, and the computational tools in turn allow us to improve and speed those same efforts. Through this process, we have successfully identified previously undescribed lexical, morphological, and phonological processes in Yupik while simultaneously increasing the coverage of the morphological analyzer. Given the polysynthetic nature of Yupik, a high-coverage morphological analyzer is a necessary prerequisite for the development of other high-level computational tools that have been requested by the Yupik community. 

St. Lawrence Island Yupik is a polysynthetic language indigenous to St. Lawrence Island, Alaska, and the Chukotka Peninsula of Russia. While the vast majority of St. Lawrence Islanders over the age of 40 are fluent L1 Yupik speakers, rapid language shift is underway among younger generations; language shift in Chukotka is even further advanced. This work presents a holistic proposal for language revitalization that takes into account numerous serious challenges, including the remote location of St. Lawrence Island and Chukotka, the high turnover rate among local teachers, socioeconomic challenges, and the lack of existing language learning materials.