Towards Unsupervised Morphological Analysis of Polysynthetic Languages (2022)

Abstract: Polysynthetic languages present a challenge for morphological analysis due to the complexity of their words and the lack of high-quality annotated datasets needed to build and/or evaluate computational models. The contribution of this work is twofold. First, using linguists’ help, we generate and contribute high-quality annotated data for two low-resource polysynthetic languages for two tasks: morphological segmentation and part-of-speech (POS) tagging. Second, we present the results of state-of-the-art unsupervised approaches for these two tasks on Adyghe and Inuktitut. Our findings show that for these polysynthetic languages, using linguistic priors helps the task of morphological segmentation and that using stems rather than words as the core unit of abstraction leads to superior performance on POS tagging.

## introduction polysynthetic languages are highly synthetic languages, where a single multi-morpheme verbal complex can express what would be a whole sentence in english. for example, in inuktitut, "tusaatsiarunnanngittualuujunga" corresponds to the english sentence "i cannot hear very well" @xcite . these languages pose two main challenges for computational models. first, they are often characterized by a significant number of morphemes per word and a high degree of ambiguity of their roots with respect to the part-of-speech specification @xcite . second, these languages are low-resource, lacking large scale annotated datasets needed to build computational models. we focus on surface-level morphological segmentation and part-of-speech tagging for two polysynthetic languages: adyghe and inuktitut. progress in morphological analysis of polysynthetic languages has been made possible by two efforts: morphological segmentation frameworks that move away from rule-based methods to un-supervised machine learning models, which crucially are able to include linguistic priors to guide the learning process @xcite @xcite , and the growth in corpora for some of these languages @xcite @xcite @xcite . a particularly fruitful line of work has been the use of unsupervised models based on adaptor grammars @xcite , such as morphagram (eskander et al., 2020a) that enables the use of linguistic priors, either through grammar definition or linguist-provided affixes @xcite . we investigate whether linguistic priors in morphagram help the task of morphological segmentation for adyghe and inuktitut. pos tagging for polysynthetic languages, on the other hand, is in its infancy. we investigate whether unsupervised approaches based on crosslingual projection developed for low-data scenarios @xcite @xcite @xcite ) could be useful for pos tagging of polysynthetic languages. these methods rely on the use of parallel data (e.g., the bible) to project pos tags from a source language for which a pos tagger is accessible onto a target language across word-level alignments. the projected tags then become the basis to train a pos model for the target language. @xcite have recently proposed an approach for cross-lingual projection in low-data scenarios, where the unit of abstraction could be either the word or the stem, thus exploring either word-level or stem-level alignments for projection 1 . we show that for adyghe and inuktitut, using stems as the unit of abstraction improves the results for pos tagging. we contribute adyghe and inuktitut evalu-ation datasets both for morphological segmentation and pos tagging. ## approach 3.1 morphological segmentation to conduct the experiments for morphological segmentation, we use morphagram foot_1 @xcite , a state-of-the-art, publicly available framework for unsupervised morphological segmentation that is based on adaptor grammars (ags) @xcite . ags are nonparametric bayesian models that utilize probabilistic context-free grammars (pcfgs). an ag is composed of two main components: a pcfg and an adaptor that adapts the probabilities of individual subtrees and acts as a caching mechanism. in the case of morphological segmentation, a pcfg represents a morphological grammar that specifies word formation, where the purpose is to learn latent tree structures of morphological segments given a list of unsegmented words. while morphagram was originally developed for learning in a fully unsupervised manner, it also allows the use of linguistic priors to enhance morphological segmentation in a minimally supervised fashion. @xcite introduce two methods for including linguistic priors: grammar definition and linguist-provided affixes. in the former, a linguist tailors the language independent grammars used by morphagram to more accurately model the word structure of the target language. in the latter, an expert in the target language compiles a list of affixes and seeds it into the grammars using the scholar-seeded learning setting @xcite . for all of our experiments and languages in this paper, we apply the second approach where linguist-provided affixes are used. we follow @xcite by applying their on-average best performing grammar, namely prstsu+sm foot_2 , in which a word is modelled as a sequence of prefixes, a stem and a sequence of suffixes, additionally, both prefixes and suffixes are recursively defined to allow for affix compounding, and the morphemes are further split into non-linguistically driven sub-morphemes that allow for better utilization of the generated latent subtrees @xcite for more details). for inuktitut, we use the affixes from inuktitut tusaalanga grammar foot_3 . ## conclusion we contribute high-quality datasets for inuktitut and adyghe, both for morphological segmentation and pos tagging. we show that unsupervised approaches that consider linguistic priors are a promising avenue for tackling morphological segmentaters for polysynthetic languages. we also show that unsupervised cross-lingual projection approaches for pos tagging that use the stem as a unit of abstraction are a fruitful avenue of research on pos tagging for polysynthetic languages.