St. Lawrence Island / Central Siberian Yupik is an endangered language, indigenous to St. Lawrence Island in Alaska and the Chukotka Peninsula of Russia, that exhibits pervasive agglutinative and polysynthetic properties. This paper discusses an implementation of a finite-state morphological analyzer for Yupik that was developed in accordance with the grammatical standards and phenomena documented in Steven A. Jacobson’s 2001 reference grammar for Yupik. The analyzer was written in foma, an open source framework for constructing finite-state grammars of morphology. The approach presented here cyclically interweaves morphology and phonology to account for the language’s intricate morphophonological system, an approach that may be applicable to languages of matching typology. The morphological analyzer has been designed to serve as foundational resource that will eventually underpin a suite of computational tools for Yupik to assist in the process of linguistic documentation and revitalization.
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Improved Finite-State Morphological Analysis for St. Lawrence Island Yupik Using Paradigm Function Morphology
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.
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- Award ID(s):
- 1761680
- NSF-PAR ID:
- 10184458
- Date Published:
- Journal Name:
- LREC proceedings
- ISSN:
- 2522-2686
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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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.more » « less
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Morphological analysis is a critical enabling technology for polysynthetic languages. We present a neural morphological analyzer for case-inflected nouns in St. Lawrence Island Yupik, an endangered polysynthetic language in the Inuit-Yupik language family, treating morphological analysis as a recurrent neural sequence-to-sequence task. By utilizing an existing finite-state morphological analyzer to create training data, we improve analysis coverage on attested Yupik word types from approximately 75% for the existing finite-state analyzer to 100% for the neural analyzer. At the same time, we achieve a substantially higher level of accuracy on a held-out testing set, from 78.9% accuracy for the finite-state analyzer to 92.2% accuracy for our neural analyzer.more » « less
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