Search for: All records

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. 

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. 

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). 

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). 

Abstract This article describes a simple PCFG induction model with a fixed category domain that predicts a large majority of attested constituent boundaries, and predicts labels consistent with nearly half of attested constituent labels on a standard evaluation data set of child-directed speech. The article then explores the idea that the difference between simple grammars exhibited by child learners and fully recursive grammars exhibited by adult learners may be an effect of increasing working memory capacity, where the shallow grammars are constrained images of the recursive grammars. An implementation of these memory bounds as limits on center embedding in a depth-specific transform of a recursive grammar yields a significant improvement over an equivalent but unbounded baseline, suggesting that this arrangement may indeed confer a learning advantage. 

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. 

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 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. 

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.