JB 132 submission to the SIGMORPHON 2022 Shared Task 3 on Morphological Segmentation (2022)

Abstract: This paper describes the JB132 submission to the SIGMORPHON 2022 Shared Task 3 on Morpheme Segmentation. In this paper we describe probabilistic model trained with the Expectation-Maximization algorithm, we provide the results and analyze sources of errors and general limitations of our approach. The model was implemented within our own modular probabilistic framework.

## introduction this paper describes jb132 submission to shared task on morphological segmentation, which is the task of segmentation of words to the smallest units carrying meaning -morphemes (e.g. prefixes, root, suffixes). our general approach was to create our own modular framework for probabilistic models trained via expectation-maximization, so that we can quickly test large number of various model architectures. we designed various probabilistic models, described them within the framework and tested them across languages. in this paper we provide the description of the best model architecture. since the algorithm achieves poor results, we further analyze its outputs and describe causes of errors it makes. ## task the shared task focused on both morphological segmentation of solitary words (task 1) and words in sentences (task 2), but we have only participated in task 1. the training data spanned across 9 languages (czech, english, french, hungarian, spanish, italian, latin, russian, mongolian) and contained tens of thousands to hundreds of thousands training samples. the structure and complexity of input data varied. the czech words were segmented to morphs (absorbovat ab-sorb-ova-t), while e.g. spanish and russian data contained segmentation to morphemes, including change of root and presence of morphemes that were used in the derivation of the word but now only map to null morphs (encuestéis encuesta-ar-éis; автоматизируемые автоматизм-ировать-уем-ый-ые). ## related work probabilistic models are commonly used in morphological segmentation, although often focused on morphs instead of morphemes and trained in unsupervised or weakly supervised settings. there are three (sometimes overlapping) groups of probabilistic models used for segmentation: the first group are bayesian models, which rely on complex generative stories, including even prior distributions of numbers of morphemes of words or prior distribution of morpheme frequencies. an interesting example of this approach is @xcite , which experimented with a joint multilingual model for several related languages and showed that it can improve the resulting segmentation in unsupervised setting. the second group are maximum a posteriori probability (= minimum description length) models. these models try to find the best compression of words (including the size of the compression model's parameters) and are usually optimized via some kind of local optimization. models of this type are e.g. @xcite and @xcite , which also use morpheme lexicons, but unlike our model consider size of the dictionary part of the loss function. the last group are expectation-maximization models such as @xcite , @xcite , which tend to make use of simpler loss functions that further simplify when em is applied and thus allow for faster computation. ## solution our approach was to create a modular framework for probabilistic models optimized via the expectation-maximization algorithm. we then described various architectures within this framework and tried to find the one that works the best across the languages. our final architecture consists of two main parts: word-→morpheme generator (fig. 1 ) which models the structure of words as sequence of morphemes and the morpheme-→morphs model (fig. 2 ) which models the morpheme realization. each of those parts is trained separately and they are then merged together. ## results the systems were evaluated via morpheme precision and recall. precision is defined as the number of correctly predicted morphemes divided by total number of predicted morphemes. recall is defined as the number of correctly predicted morphemes divided by total number of morphemes in the golden segmentation. the following table summarizes our f-scores on the languages, as they were measured by the organizers of the shared task in @xcite . we can see that the model achieves relatively good results only on latin (which was segmented to morphs) and not on other languages. ## conclusion & future work our submission to the shared task on morphological segmentation was a modular probabilistic model trained via em. the model has achieved poor results and the error analysis shows that a big amount of modifications will be needed in order to improve the results. especially, the addition of more contextual information will be necessary. it also remains unclear how to handle differences between root morphs and root morphemes with this type of model.