KU - CST at the SIGMORPHON 2020 Task 2 on Unsupervised Morphological Paradigm Completion (2020)

Abstract: We present a model for the unsupervised dis- covery of morphological paradigms. The goal of this model is to induce morphological paradigms from the bible (raw text) and a list of lemmas. We have created a model that splits each lemma in a stem and a suffix, and then we try to create a plausible suffix list by con- sidering lemma pairs. Our model was not able to outperform the official baseline, and there is still room for improvement, but we believe that the ideas presented here are worth considering.

## introduction in this paper we describe our attempt to capture morphological paradigms totally from scratch @xcite prepared for the task of morphological paradigm completion in the conll-sigmorphon 2020 shared task. computational morphology is not a new area and there is plenty of related work. some years ago, this problem was commonly tackled using finite-state and two-level approaches, such as in @xcite , @xcite , and @xcite . recent works, on the other hand, rely mostly on statistical approaches, such as in @xcite and @xcite . there have been several shared tasks recently on morphological inflection @xcite @xcite . the task for this year is more complex, as we are asked to discover paradigms from scratch. this is an intriguing research area that could give us the chance of recovering dead languages that have only limited written resources. several researchers have attempted to solve this task, such as @xcite , @xcite , and @xcite . we present a pipeline that assumes that all morphological realizations in a paradigm (for each language) follow a fixed structure: stem+suffix. based on that logic, we look for the best candidates to compose the suffix inventory, we cluster them using k-means and after that, we join stems and suffixes. we employ language models to get the most natural outputs. the pipeline that we have developed does not contain any neural network component, but we contemplate it as a possibility to extend our work in the near future. this paper is structured as follows: in the next section we introduce the task that we have worked on. we describe our approach in the third section. afterwards, we show our results compared to the baseline model. to conclude, we discuss our results and provide possible future directions. ## task in this competition there was one task that we had to perform. a computational system had to be built, which, given a raw text and a set of lemmas, it would return the complete list of paradigms for each verb. the computational model should be able to read a text like this, the aircraft landed at the jfk airport. other pilots decided to land in philadelphia. as you may imagine, landing a plane is not an easy job, but imagination can help. and extract morphological paradigms. in the shared task, a list of lemmas is also given as a starting point. this list of lemmas could include verbs like land and imagine. in the case of the verb land, in the example above, it is pretty easy to get its inflections (land, landed, landing). this could, for example, be done with a minimum edit distance based method and it is relatively easy, as there is no usage of land with the function of a noun. it gets slightly more complicated with the verb imagine, as a simple distancebased algorithm could fail, because it could find imagination as a possible conjugation of the verb imagine. ## method our method has a very strong assumption, which oversimplifies the problem but it also gives the chance of recognizing some patterns. the assumption is that all lemmas and their inflections have the following form for all languages stem +@xmath0: ## discussion and future work we have presented our approach for automatically discovering morphological paradigms, given a text and list of lemmas. as mentioned above, our results are behind the official baseline, and therefore, there is a wide range of possibilities for improvement. we discuss some of them below. we assumed each inflected form to be decomposable into a stem and a suffix. this could be, for example, sufficient for english or spanish, but not for languages such as german that follow a two splits pattern: stem +@xmath1, for example, participles are formed by prefixing ge: @xmath2, a much more straightforward estimate of the morphological richness r m could, for example, be obtained by just considering the triple l 1 = r1 +s, l 2 = r2 +s, l 3 = r3 +s of optimally splitted distinct lemmas with the maximum number of common suffixes. because these lemmas are most likely to be frequently used lemmas with regular inflection, the size of the union of their inflections would presumably yield a good estimate of r m . clustering of these triples could also help in identifying verb classes with distinct but regular inflection. moreover, splitting of compound verbs of the form x+v, with x typically a noun or verb, would certainly improve performance because the inflec-tions of the verb v could be used for the typically less frequent compound verb x+v. with respect to the writing system, the basque bible follows old orthographical rules. on the other hand, the lemmas were written following more recent orthography rules. this lack of consistency makes the task a challenge, and we expect it to happen in other languages as well. this issue requires special attention, by maybe applying some preprocessing to the lemmas to accommodate to the old writing system @xcite . also, we mentioned at the beginning of the article that we have not used any neural network based component, and these would be very useful for learning the morphophonological changes that commonly happen when inflecting words. therefore, we would like to incorporate a sequenceto-sequence model at the end of our pipeline @xcite @xcite .