Does Topological Ordering of Morphological Segments Reduce Morphological Modeling Complexity? A Preliminary Study on 13 Languages (2023)

Abstract: Generalization to novel forms and feature combinations is the key to efficient learning. Recently, Goldman et al. (2022) demonstrated that contemporary neural approaches to morphological inflection still struggle to generalize to unseen words and feature combinations, even in agglutinative languages. In this paper, we argue that the use of morphological segmentation in inflection modeling allows decomposing the problem into sub-problems of substantially smaller search space. We suggest that morphological segments may be globally topologically sorted according to their grammatical categories within a given language. Our experiments demonstrate that such segmentation provides all the necessary information for better generalization, especially in agglutinative languages.

## introduction generalization is a form of abstraction where common patterns, or properties, that are observed across specific instances are then extended to a wider class of instances. this form of deductive inference allows humans to learn language more efficiently, form sophisticated concepts, and introduce semantic relations such as hypernymy. still, computer systems are considered to be less successful in making generalizations from data @xcite . morphological inflection task is a popular playground to compare and evaluate systems' ability to generalize. the morphological inflection task is a type of language modelling that focuses on producing inflected forms from a given dictionary form (a lemma) and a set of morphosyntactic features (a tagset) that describes the word form to be produced, as in "spider, (n ; p l) → spiders". in this paper, we suggest that annotated morphological segmentation can significantly improve the generalization ability. we propose augmenting the inflection model with segmentation as an intermediate step. we aim to evaluate the claim that such task is easier to solve than the reinflection task in its classical setting, especially in agglutinative languages. we suggest that the reinflection task can be formalized as a classification task rather than a string-to-string transduction task. this approach dramatically reduces the search space during the inference phase as well as enhances the model's robustness to data sparsity. ## the dataset in our experiments we used datasets for inflectional paradigms and segmentation for catalan (cat), czech (ces), german (deu), english (eng), finnish (fin), french (fra), hungarian (hun), italian (ita), mongolian (mon), portuguese (por), russian (rus), spanish (spa), and swedish (swe) provided in mor-phynet resource @xcite . ## learning the order of segments we hypothesise that the order of morphological segments foot_0 within a language is defined by the order of their corresponding grammatical categories (such as grammatical number, person, case). for instance, turkish nouns would first specify the number and then the case (as shown on in the dataset described above, each word form w j stands for a sequence of [(s i , t i )] j , where s i is i-segment in word form j, t i is a tagset describing the segment (segmental tagset; such as "gen ; p l" for fusional or "gen " for agglunitative languages). let us illustrate this notation by the following example from catalan, taken from morphynet dataset. ## decomposing tagsets as grammatical feature combinations are often complex, one might expect that there should be numerous ways to decompose those corresponding to morphological segments, thus, making decomposition a separate complex subtask. in this section, we refute it by demonstrating the statistics on decomposition variety per distinct segmental tagset. as both segments and their corresponding tagsets are listed for each word form in mor-phynet, it may appear that a "natural" way of segmentation modelling would look as follows. first, decompose the initial tagset into segmentwise sub-combinations and, second, map each subcombination into a distinct morphological segment. however, as we discovered, this technique does not work well because the assignment of tag combinations to segments appears to be highly ambiguous in morphynet. in many cases, it is due to the tags that represent an inherent property of a lemma. these tags, therefore, are not realized as a segment (e.g., animacy in nouns). the lack of consistent rules governing tag-to-segment annotation is another source of ambiguity as it frequently leads to different tagging across similar samples. fortunately, there is an alternative technique that works better. namely, we should consider unique combinations of resulting moprhological segments rather than focus on the variants of tagset decomposition. our experiments demonstrate that the number of distinct morphological segments per tagset is less than 4 for the majority of tag combinations, and only in 5% cases reaches 15 (in total, approximately 2,400 tag combinations were considered, as counted separately for each language). the stem segment of any word was replaced by a wildcard symbol matching with other word stem segments. foot_1 figure 1 shows the distribution of the number of segment variants per a distinct tag combination. it is worth mentioning that more than a half of tag combinations that are realized by "15 or more segment sequences" each were russian verb forms. the first letter of suffixes in those verbs may depend on the adjacent ending of the verb's stem. this dependency results in either copying of a stem trailing consonant or a consonant mutation. thus, it is necessary to take adjacent letter into account in order to predict the segment correctly. ## segment composability in section 3 we have demonstrated that the order of segments is deterministic. still, in the condition when the data is sparse an inflection system should be able to retrieve relevant segments from training samples, especially in agglutinative languages. typically, the observed tagsets are different from the one that needs to be predicted. we define a "segment composability" measure over a segmentation dataset as a percentage of tagsets t with the following property: the segment has ever been seen in at least two data samples, one with tagset t and one, with tagset t ′ ̸ = t. while evaluating this percentage, we prune all tagsets that contain tags that only occur once, i.e. in that particular tagset (which means the tagset cannot be reconstructed from the rest of the data). a "segment composability" is a probability for a segmentation corresponding to the tagset to be reconstructed from segments observed in other tagsets, given that the predictor uses a "perfect" oracle over segments observed in a training set. the composability values measured over morphynet are provided in ## from segments to surface forms even when all morphological segments are predicted, a conversion into a surface form is yet to be done. luckily, in most cases, such a conversion only requires to remove segment separators and concatenate the substrings. however, to account for phonotactics, additional string edit operations may be necessary. our analysis discovered the following major cases when they are needed: ## conclusion we conducted a series of experiments with morphological segmentation and demonstrated that annotated segment sequences may significantly simplify the prediction of inflected forms. we outlined that inflection task can be transformed from sequenceto-sequence into a classification task, with better capacities to address language agglutinativity challenges.