Morphological Segmentation for S eneca (2021)

Abstract: This study takes up the task of low-resource morphological segmentation for Seneca, a critically endangered and morphologically complex Native American language primarily spoken in what is now New York State and Ontario. The labeled data in our experiments comes from two sources: one digitized from a publicly available grammar book and the other collected from informal sources. We treat these two sources as distinct domains and investigate different evaluation designs for model selection. The first design abides by standard practices and evaluate models with the in-domain development set, while the second one carries out evaluation using a development domain, or the out-of-domain development set. Across a series of monolingual and crosslinguistic training settings, our results demonstrate the utility of neural encoder-decoder architecture when coupled with multi-task learning.

## introduction a member of the hodinöhšöni (iroquoian) language family in north america, the seneca language is spoken mainly in three reservations located in western new york: allegany, cattaraugus and tonawanda. seneca is considered acutely endangered and is currently estimated to have fewer than 50 first-language speakers left, most of whom are elders. motivated by the seneca community's language reclamation and revitalization program, a few hundred children and adults are actively learning and speaking seneca as a second language. to further facilitate the documentation process of seneca, recent years have witnessed the scholarly bridge between the language community and academic research, taking advantage of rapidly evolving technologies in natural language processing (nlp) @xcite . in particular, ongoing work has mainly been devoted to developing automatic speech recognition (asr) systems for seneca @xcite . their findings demonstrated that when combined with synthetic data augmentation and machine learning techniques, robust acoustic models could be built even with a very limited amount of recorded naturalistic speech. more importantly, the research output was incorporated into the seneca people's documentation endeavors, illustrating the potential of collaborations between language communities and academic researchers. the current study contributes to this line of research with the same ethical considerations @xcite . specifically, we focus on morphological segmentation for seneca, an area that has not yet been investigated thus far. given a seneca word, the task of morphological segmentation is to decompose it into individual morphemes (e.g., hasgatgwë'@xmath0. with a series of in-domain, cross-domain and cross-linguistic experiments, the goal of our work is to build effective segmentation models that can support the community's ongoing language reclamation and revitalization efforts. particularly for morphologically rich languages, it has been shown that morphological segmentation is a useful component in certain nlp tasks such as machine translation @xcite , dependency parsing @xcite , keyword spotting @xcite , and automatic speech recognition (asr) @xcite . given that seneca is a highly polysynthetic language (see section 2), good morphological segmentation models show promise for the development of other computational systems such as asr, which would facilitate the documentation process of the language itself. another motivation for our experiments lies in the fact that previous research on morphological segmentation has mostly concentrated on indo-european languages in high-resource settings @xcite @xcite , sometimes relying on external large-scale corpora in order to derive morpheme or lexical frequency information @xcite @xcite . by contrast, work on morphological segmentation of augmented low-resource settings or truly underresourced languages is lacking in general @xcite . hence demonstrations of what model architecture and training settings could be beneficial with data sets of very small size would be informative to other researchers whose work shares similar goals and ethical considerations as ours. ## data statements following recently advocated scientific practices @xcite , we would like to first introduce the data of the indigenous languages to be explored. the protagonist in our experiments is seneca, the data of which came from three sources: the book the seneca verb: labeling the ancient voice by @xcite 1 , informal transcriptions provided by members from the community, and a recently digitized bible translated into seneca. the grammar book provides morphological segmentation for only verbs and the morpheme boundaries were based on rules defined by grammarians. by contrast, the informal sources contain labeled segmentation for a mix of verbs and nouns conducted by community speakers. the bible offers only unlabeled data. one of the most distinct features of seneca morphology is that it is highly polysynthetic. this means that a single word can consist of multiple morphemes and may contain more than one stem; and this single word is able to express the meaning of a whole phrase or even sentences at times @xcite . as a demonstration, consider the following example (the indicated morphological characteristics here abide by the annotation standards of sylak-glassman (2016)). breaking the seneca word into individual morphemes, ye:nö is the stem which has the verbal meaning of grab in present tense; the prefix ke denotes that ye:nö is a transitive action, with i being the subject and her/them being the object; the single apostrophe ' at the end marks the a large number of words in seneca have agglutinative morphological features, meaning when multiple morphemes are combined during word formation, their original forms remain unchanged. consider the example presented above again. when the prefix and the stem are combined into the word, neither of them goes through any phonological and orthographic changes. on the other hand, seneca also has fusional properties; this means that during the formation of some words, the combining morphemes can undergo phonological (and orthographical) changes. as an illustration, consider the following word in seneca. when combining the four morphemes together, the masculine singular subject hra, the verb stem k and the s that marks habitual state do not undergo any changes; whereas the initial i is replaced with í to make sure that the verbs or verb phrases have at least two syllables (chafe, 2015). ## related work the task of morphological segmentation has been cast in distinct ways in previous work. one line of research focuses on surface segmentation @xcite , while the other attends to canonical segmentation @xcite . both involve correctly decomposing a given word into distinct morphemes, which also typically includes words that stand alone as free morphemes. nevertheless, the two tasks differ in one key aspect: whether the combination foot_0 of the segmented morpheme sequence stays true to the initial orthography of the word. for surface segmentation, the answer is yes (e.g., indonnesian @xmath1. on the other hand, canonical segmentation sometimes involves the addition and/or deletion of characters from the surface form of the initial word, in order to capture phonological or orthographic characteristics of the component morphemes when uncombined. for example, the word measurable in english would be segmented as measure + able, recovering the orthographic e that was lost during word formation. for surface segmentation, both supervised and unsupervised approaches have gained in popularity over the years. within the realm of supervised methods, a large number of experiments have developed rule-based finite-state transducers (fst) @xcite with weights usually determined by rich linguistic feature sets. the high functionality of hand-crafted fst for morphological analyses has been demonstrated for languages such as persian @xcite , finnish @xcite , semitic languages such as tigrinya @xcite and arabic @xcite , as well as various african languages @xcite . other work has shifted to more data-driven machine learning techniques, including but not limited to memory-based learning (van den @xcite , conditional random field models (crf) @xcite @xcite , and convolutional networks @xcite . unsupervised methods have perhaps enjoyed a longer history @xcite , with earlier studies relying on information-theoretic measures as indexes of character-level predictability, which were then used to determine morpheme boundaries @xcite . later work such as linguistica @xcite and morfessor @xcite applied the analyses of minimum description length for morpheme induction @xcite . @xcite developed bayesian generative models that would also take into account the context of individual words, which were able to simulate the process of how children learn to segment words given child-directed speech. in contrast to surface segmentation, the problem of canonical segmentation has mainly been addressed with supervised methods. @xcite extended a previous semi-crf @xcite for surface segmentation to jointly predict morpheme boundaries and orthographic changes, leading to improved results for german and indonesian. with the same datasets, @xcite adopted character-based neural sequence models coupled with a neural reranker, presenting further improvement from @xcite . there has, however, been some unsupervised induction of canonical segmentation (see @xcite for a thorough review). for instance, @xcite showed that certain spelling rules (e.g. insertion, deletion) derived heuristically from corpus frequency were able to handle orthographic changes during word formation. in comparison, @xcite provided a bayesian model that formulate spelling rules probabilistically with character-level contextual information; the simultaneous learning process of both the rules and morpheme boundaries in turn boosted segmentation performance. although seneca has fusional morphological features, meaning that certain morpheme boundaries within words are not necessarily clear-cut, the seneca morphological data currently does not provide labeled canonical segmentation. we therefore focus on the task of surface segmentation. ## conclusions and future work we have investigated morphological segmentation for seneca, an indigenous native american language with highly complex morphological characteristics. in a series of in-domain, cross-domain, and cross-linguistic training settings, the results demonstrate that neural seq2seq models are quite effective at correctly labeling morpheme boundaries, at least at the surface level. with the two evaluation designs explored here, the model settings were able to achieve above 96% f1 score for data from the grammar book, and above 85% for the informal sources. many of the languages indigenous to north america are as endangered as seneca and have available resources comparable in both size and scope to those used in the current work. our thorough investigation of how to effectively integrate these limited and varied resources can potentially serve as a model for other community-driven collaborations to document endangered languages for future generations, and to produce materials suitable for language immersion and revitalization. for our future work, in addition to refining and improving our models, we also plan to explore the utility of morphological segmentation for improving language modeling in asr. this would be able to support transcription of both archival recordings and new recordings captured by community members involved in language revitalization projects.