Unsupervised Paradigm Clustering Using Transformation Rules (2021)

Abstract: This paper describes the submission of the CU-UBC team for the SIGMORPHON 2021 Shared Task 2: Unsupervised morphological paradigm clustering. Our system generates paradigms using morphological transformation rules which are discovered from raw data. We experiment with two methods for discovering rules. Our first approach generates prefix and suffix transformations between similar strings. Secondly, we experiment with more general rules which can apply transformations inside the input strings in addition to prefix and suffix transformations. We find that the best overall performance is delivered by prefix and suffix rules but more general transformation rules perform better for languages with templatic morphology and very high morpheme-to-word ratios.

## introduction supervised sequence-to-sequence models for word inflection have delivered impressive results in the past few years and a number of shared tasks on supervised learning of morphology have helped to raise the state of the art of this task @xcite @xcite @xcite . in contrast, unsupervised approaches to morphology have received far less attention in recent years. nevertheless, the question of whether the morphological system of a language can be discovered from raw text data alone is certainly an interesting one. this paper describes the submission of the cu-ubc team for the sigmorphon 2021 shared task 2: unsupervised morphological paradigm clustering @xcite . 1 the objective of this task is to group 1 github.com/changbingy/sigmorph-2021-task2 the distinct inflected forms of lexemes occurring in a corpus into morphological paradigms. figure 1 illustrates the task. our system generates paradigms using morphological transformation rules which are discovered from raw data. as an example, consider the rule @xmath0, which maps an english past tense verb form like walked into the present participle walking. in this paper, we use regular expressions of symbol-pairs (that is, regular relations) in the well-known xerox formalism @xcite to denote rules: for example, ?+ e:i d:n 0:g. these rule can be applied using composition of regular relations: we experiment with two methods for discovering rules, described in section 3.3. our first approach is inspired by work on morphology discovery by @xcite , who generate prefix and suffix transformations between similar strings. this idea closely parallels our approach for extracting rules. unlike @xcite , however, we do not utilize word embeddings when extracting rules due to the very small size of the shared task datasets. in addition to prefix and suffix rules, we also experiment with more general discontinuous transformation rules which can apply transformations to infixes as well as prefixes and suffixes. for example, the rule ?+ i:0 ?+ e:i ?+ 0:t would transform the input form gidem ('to bite' in maltese) to gdimt. our results demonstrate that prefix and suffix rules deliver stronger performance for most languages in the shared task dataset but our more general transformations rules are beneficial for templatic languages like maltese and languages with a high morpheme-to-word ratio like basque. ## related work the unsupervised paradigm clustering task is closely related to the 2020 sigmorphon shared task on unsupervised morphological paradigm completion @xcite . however, paradigm clustering systems do not infer missing forms in paradigms. our system resembles the baseline system for the paradigm completion task @xcite which also extracts transformation rules, however, in the form of edit trees @xcite . several approaches to unsupervised or minimally supervised morphology learning, which share characteristics with our system, have been proposed. our rules are essentially identical to the fst rules used by @xcite for the task of supervised morphological inflection. likewise, @xcite and @xcite both extract inflectional rules after aligning forms from known paradigms. @xcite also generate rules for morphological transformations but their system for minimally supervised morphological analysis requires additional information in the form of a list of morphemes as input. erdmann et al. (2020) present a task called the paradigm discovery problem which is quite similar to the unsupervised paradigm clustering task. in their formulation of the task, inflected forms are clustered into paradigms and corre-sponding forms in distinct paradigms (like all plural forms of english nouns) are clustered into cells. their benchmark system is based on splitting every form into a (potentially discontinuous) base and exponent, where the base is the longest common subsequence of the forms in a paradigm and the exponent is the residual of the form. they then maximize the base in each paradigm while minimizing the exponents of individual forms. ## methods this section describes how we extract rules from the dataset and apply them to paradigm clustering. we also describe methods for filtering out extraneous forms from generated paradigms. ## experiments and results in this section, we describe experiments on the shared task development and test languages. ## discussion and conclusions paradigm construction can suffer from two main difficulties: overgeneralization, and underspecification. in the former, paradigms are too generous when adding new members. consider, for example, a paradigm headed by "sea". we would want to include the plural "seas", but not the unrelated words "seal", "seals", "undersea", etc. contrarily, a paradigm selection algorithm that is overly selective will result in a large number of small paradigms -less than ideal in a morphologically-dense language. considering the results described in the previous section, we note that our two best models skew towards conservatism -they prefer smaller paradigms. this is likely an artifact of our development cycle -we found that the baseline preferred large paradigms, often capturing derivational features, or even circumstantial our methods were also affected by the choice of development languages. of these languages, only one (persian) is agglutinating, and none of the authors can read the script, so it had a smaller impact on the evolution of our methods. we believe that several languages -namely, finnish, turkish, and basque-could have benefited from iterative rule application; however, the iterative process was not selected after seeing a degradation (due to overgeneralization) on the development languages. it is also worth discussing two outliers in our system selection. our suffix-first model performed very well on all of the development languages except maltese. this is not surprising, given its templatic morphology. maltese inspired the creation of our discontinuous rule set, and indeed, these rules outperformed the suffixes for maltese. switching to the test languages, we see that this model has higher performance for navajo and basque -two languages that are rarely described as templatic. we observe, however, that both languages make heavy use of prefixing. note in there is still plenty of ground to be covered, with the mean f1 score below 70%. we believe that the next step lies in re-establishing a bottom-up construction for those paradigms that our methods currently separate into small sub-paradigms. our methods predict roughly twice to 3 times as many singleton paradigms as exist in the gold data, and there is not signifi-cant rule support to combine them. possible areas for exploration include iterative rule extraction on successively more correct paradigms, or the incorporation of a machine learning element that can predict missing forms. in this paper, we have presented a method for automatically building inflectional paradigms from raw data. starting with an n-gram baseline, we extract intra-paradigmatic rewrite rules. these rules are then re-applied to the corpus in a discovery process that re-establishes known paradigms. our methods prove very competitive, with our best model finishing within 2% of the best submitted system.