# A Benchmark for Morphological Segmentation in Uyghur and Kazakh
###### Abstract
The differing spelling of morphemes after harmony could increase out-of-vocabulary words, impacting the model's generalization ability. This characteristic distinguishes the morphological segmentation task in agglutinative languages from the word segmentation or morphological segmentation tasks in other languages, such as Chinese or English. Therefore, morphological segmentation models designed for other languages may not perform well when applied to morphologically rich languages like Uyghur or Kazakh. Due to the agglutinative nature of Uyghur and Kazakh, theoretically, an infinite vocabulary can be generated [7]. As a result, data sparsity in agglutinative languages poses a challenge for downstream NLP tasks, as even small datasets lead to a large vocabulary [5]. However, morphological segmentation, which divides words into their smallest semantic units while maintaining semantic information, effectively alleviates the data sparsity issue caused by rich morphology. Therefore, morphological segmentation and stemming are widely used in various downstream natural language processing tasks such as named entity recognition [8], keyword extraction [4], question answering [9], speech recognition [10], machine translation [11; 12], and language modeling [3]. The sequence labeling task is a fundamental problem in NLP, which involves assigning a label to each element in an input sequence. In Uyghur and Kazakh, morphological segmentation or stemming are often considered character-level sequence labeling problems, and models predict at the character-level labels. Therefore, character-level evaluation methods are commonly used. However, character-level evaluation cannot reflect the overall performance of models in agglutinative language morphology segmentation, exhibiting some shortcomings. As shown in Table 1, the correct segmentation of the word \"_Shi-Cul-_\" is \"_Shi-Cul-_\", true labels are \"BMMMMEBME\", consisting of two morphemes. In labels, \"B\" represents the starting character of a morpheme, \"M\" represents the middle characters of a morpheme, \"E\" represents the ending character of a morpheme. However, the model incorrectly predicted the label of the eighth character as E, where the prediction was wrong by only one label while the rest were correct. Using a character-based method, an incorrect label will not affect the morpheme to which the character belongs. However,  
Figure 1: Examples of segmentation and stemming. when characters are merged into morphemes, it can be seen that this error label affects the morphemes \",\" and \". Although current state-of-the-art (SOTA) models have achieved around 97% accuracy in character-level evaluation, the evaluation methods used only focus on individual characters (a single point) and do not take into account the morphological context of the characters (the horizontal relationships between points). The effect of model prediction errors on cutoff results. To enhance the performance of stemming and morphological segmentation models for Uyghur and Kazakh, this paper redefines morpheme-based evaluation metrics (F1-score and accuracy) in morphological segmentation and stemming. In addition, we propose two benchmark models based on different training methods: (1) a supervised model--Feature-Enhanced Morphological Segmentation Model (FEMSeg) for morphological segmentation and stemming; (2) an unsupervised morphological segmentation model--Masked Morphological segmentation (MMSeg). The character-level and contextual features are learned in the supervised model through CNN and BiLSTM networks, representing the input embedding. This embedding is then fed into the encoder of the Transformer model, where linear transformations and multi-head attention mechanisms are used to learn the relationships between character features, contextual features, and morphological boundaries in different spaces.