To compress or not to compress? A Finite-State approach to N en verbal morphology (2020)

Abstract: This paper describes the development of a verbal morphological parser for an under-resourced Papuan language, Nen. Nen verbal morphology is particularly complex, with a transitive verb taking up to 1,740 unique features. The structural properties exhibited by Nen verbs raises interesting choices for analysis. Here we compare two possible methods of analysis: ‘Chunking’ and decomposition. ‘Chunking’ refers to the concept of collating morphological segments into one, whereas the decomposition model follows a more classical linguistic approach. Both models are built using the Finite-State Transducer toolkit foma. The resultant architecture shows differences in size and structural clarity. While the ‘Chunking’ model is under half the size of the full de-composed counterpart, the decomposition displays higher structural order. In this paper, we describe the challenges encountered when modelling a language exhibiting distributed exponence and present the first morphological analyser for Nen, with an overall accuracy of 80.3%.

## introduction with the advance of modern technology, collecting data for the task of language documentation has become easier, but methods for coping with the influx of data have become a pressing concern. one robust solution in the realm of morphology and phonology has been finite state methods. this paper focuses on the development of finite-state architecture in aid of the glossing process for building resources for nen. nen is a underresourced language of the morehead-maro language family of southern new guinea @xcite . it is spoken by approximately 300-350 people in the village of bimadbn in the western province of papua new guinea. the resources developed here feed directly into the efforts of documentation and corpus building. this effort is globally shared amongst fieldworkers and descriptive linguistics across many languages, in response to the estimation for half of the world's languages to be extinct within the next century @xcite . aside from aiding the documentation process, the linguistic property of multiple exponence (me) makes nen an interesting case study for computational methods, as well as exasperating the already present data sparsity problem. though much of the recent work in natural language processing (nlp) has centred around machine learning, it is still not quite feasible in low resource problem sets. neural networks remove the need for incorporating detailed knowledge of the specific context by optimizing the mapping between input/output pairs. as a consequence a large amount of training data is required @xcite . in the low resource language setting, often linguistic insight can be exploited to help generate larger datasets, such as finite-state methods being used to produce labelled data for training of neural networks @xcite . finite-state transducers (fsts) are widely accepted as a standard way to computationally model the morphological structure of words in natural languages @xcite . prior works include fsts for agglutinating languages such as turkish, tuvan, and northern haida @xcite @xcite , and more recently so-called polysynthetic languages like chukchi, kunwinjku, central siberian yupik, and arapahoe (andriyanets @xcite @xcite . the novel contributions of this paper are twofold: first, we present a preliminary morphological analyser for verbs in nen. in addition to resource building for the nen language, this work outlines a computational approach for modelling the linguistic phenomenon of distributed exponence. ## the nen language with on-going documentation efforts, the nen corpus is approximately 30, 000 words of natural speech, of which there are approximately 6, 000 verbs tokens @xcite . over a third of these verb tokens (2, 379 tokens) are varieties of the copula, which form a restricted paradigm of their own. simply put, the amount of data is scarce. to add to this problem, nen exhibits complex verbal morphology. in fact, verbs are morphologically the most complicated word-class in nen @xcite . despite this, they are often regular, allowing for generalisation of rules to analyse them. as outlined by @xcite , nen verbs can be divided into two categories: prefixing and ambifixing verbs. prefixing verbs mark the undergoer argument by prefix and ambifixing verbs employ both prefixes and suffixes to index person and number of up to two arguments. in this paper, we focus on the more complicated case of the ambifixing verb. the full prefix and suffixal paradigm can be found in @xcite the undergoer prefixes are divided into arbitrarily labelled series α, β, γ, which do not correspond to specific semantic values until they are unified with other tam (tense, aspect, and mood) markings on the verb @xcite . following the undergoer prefixes, a directional prefix slot is available. this can be filled with {-n-} 'towards', {-ng-} 'away' or left empty to convey a directionally neutral semantic. consider the verb armbs 'to climb'. when marked for direction the resultant forms are as follows: n-armb-te '(s)he is ascending (neutral)', n-n-armb-te '(s)he is coming up (towards speaker)', and n-ng-armb-te '(s)he is going up (away from speaker)'. the middle prefixes simply mark the verb as a member of the middle verb type; essentially dynamic monovalent verbs. prefix cells with more than one entry note possible allomorphy depending on the phonological environment within the verb. the suffixal system applies to both middle and transitive verb types. although it is convenient to segment verbs, into prefix, stem, and suffix, the nen verbal system distributes information in a complicated way. the prefixes and suffixes are not independent values. nen exhibits a particular kind of multiple exponence (me), which requires prefixes and suffixes to be unified before inflectional values are known @xcite . the possible combinatorial space for transitive and middle verbs is determined by summing the forms associated with each series (α, β, and γ) and the tam suffixes they can co-occur with. the figure obtained is then multiplied by the possible undergoer prefixes (with only three available to the middle verbs). lastly, this number is multiplied by three for each directional prefix available. this process yields a 1, 740 cell paradigm size for the transitive verbs. ## method several implementations of fsm compilers were available: xfst (xerox finite-state transducer) @xcite , foma @xcite , and hfst (helskini finite-state transducer) @xcite , of which the latter two are open source. to develop a morphological analyser for nen, we employed the foma finite-state toolkit. fsts are an ideal tool for morphology, since they allow for both analysis and synthesis, meaning the user can both decompose a word and construct one, given the desired morphological features. additionally, given the ongoing nature of language documentation, linguistic rules are constantly being added to, reviewed and revised. the incremental modularisation of fsts allows for easy testing of set rules and addition of new rules. fsts are constructed in two parts: the first part deals with morphological rules and irregularities, as well as lexicon creation. the second component implements morphophonological rules. ## results the decomposition model showed a clearer level of organization than the 'chunking' model (figures 1 and 4, both with the flags included). note that, one verb stem armbs 'to ascend' was used in both figures, for visibility of manifestation of morphological paradigm for one ambifixing verb. the particular stem was chosen because we had a full paradigm elicitation from members of the nen community to confirm the existence of predicted forms. when comparing the specifications of both models, shown in these results questioned the benefit of decomposing further, apart from the obvious benefit of following the linguistic description. given the added difficulty of implementing, if both yield comparable results, and the end goal is to have the highest possible accuracy of gloss than the choice of model should not matter. ## conclusion this paper explores options for modeling the low-resource language nen using finite-state transducers. nen shows distributed exponence; multiple morphs can contribute to the specification of a particular feature value. this property motivates the comparison between a 'chunking' model, which combines the thematic and desinence segment, to a decomposition model which handles the two separately at the cost of many more parameters. both models achieve the same accuracy of 80.3%. the choice of model depends on the primary concern of the user. assuming that either segmentation is linguistically possible, if the size of the transducer is of concern (as a result of the size of lexicon, complexity of rules or sheer number of rules) a 'chunking' approach can be taken with no cost to accuracy. if the user, prefers structural granularity or a one-to-one mapping between the computational implementation and the linguistic grammar then the decomposition approach can be taken. most often, the primary use of fst grammars are to provide morphological glosses, in this case there is no computational motivation for having a high resolution description. future work would entail analysing and implementing more detailed underlying morphonological rules, and investigating the cross-over from fsts to neural models. one of the prime motivations for building an fst, in the era of neural networks is to generate enough labelled data, in the appropriate format to enable testing across architectures. additionally, the process of building an fst proves to be a great way to examine the validity of the linguistic analyses.