# A Benchmark for Morphological Segmentation in Uyghur and Kazakh
## 2 Related Work
### Results
#### 4.5.1 Morphological Segmentation Comparison
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Lang.**} & \multicolumn{6}{c}{**Uyghur**} & \multicolumn{6}{c}{**Kazakh**} \\ \cline{2-13}  & \multicolumn{3}{c}{**Validation**} & \multicolumn{3}{c}{**Test**} & \multicolumn{3}{c}{**Validation**} & \multicolumn{3}{c}{**Test**} \\ \cline{2-13}  & **R** & **P** & **F1** & **R** & **P** & **F1** & **R** & **P** & **F1** & **R** & **P** & **F1** \\ \hline Morfessor & 34.68 & 31.19 & 32.84 & 35.17 & 30.73 & 32.80 & 29.25 & 26.35 & 27.72 & 28.80 & 25.66 & 27.14 \\ BPE & 6.28 & 5.76 & 6.01 & 19.01 & 16.92 & 17.91 & 26.44 & 25.04 & 25.72 & 23.92 & 23.35 & 23.63 \\ MMSeg & **49.46** & **44.88** & **47.06** & **49.99** & **45.34** & **47.55** & **43.39** & **27.91** & **33.97** & **44.71** & **29.05** & **35.22** \\ \cline{2-13}
10-flod & 48.84 & 44.84 & 46.75 & 48.73 & 45.07 & 46.75 & 46.12 & 35.53 & 40.09 & 46.20 & 35.69 & 40.22 \\ \hline BiLSTM & 90.53 & **90.26** & 90.40 & 91.58 & **91.45** & 91.51 & 91.17 & 89.54 & 90.35 & 91.57 & 90.37 & 90.97 \\ BiGRU & 91.02 & 88.57 & 89.78 & 91.85 & 89.13 & 90.47 & 90.62 & 90.16 & 90.39 & 90.23 & 89.61 & 89.91 \\ FEMSeg & **91.85** & 89.88 & **90.85** & **93.02** & 91.09 & **92.04** & **92.08** & **91.96** & **92.02** & **92.53** & **92.14** & **92.34** \\ \cline{2-13}
10-flod & 92.11 & 90.52 & 91.32 & 92.26 & 90.86 & 91.55 & 91.19 & 90.26 & 90.72 & 91.19 & 91.85 & 91.82 \\ \hline HMM & 47.60 & 36.87 & 41.55 & 47.67 & 36.65 & 41.44 & 45.84 & 36.76 & 40.80 & 47.07 & 37.75 & 41.90 \\ CRF & 82.91 & 82.28 & 82.60 & 88.76 & 88.16 & 88.46 & 81.40 & 79.70 & 80.54 & 81.12 & 79.38 & 80.24 \\ BiLSTM-CRF & 90.80 & 91.00 & 90.90 & 91.62 & 92.22 & 91.61 & 89.55 & 90.57 & 92.32 & 90.28 & 91.29 \\ BiLSTM-ATT-CRF & 91.70 & 91.14 & 91.42 & 91.76 & 91.06 & 91.41 & 91.36 & 90.55 & 90.95 & 91.44 & 90.74 & 91.09 \\ FEMSeg-CRF & **92.62** & **91.85** & **92.24** & **93.08** & **92.29** & **92.69** & **92.58** & **92.47** & **92.52** & **93.05** & **92.63** & **92.84** \\ \cline{2-13}
10-flod & 92.74 & 91.22 & 91.97 & 92.82 & 91.27 & 92.04 & 92.65 & 92.08 & 92.36 & 92.48 & 92.65 & 93.56 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparative experimental results of morphological segmentation (%). Bold represents the best result in the same group of experiments. set, with less improvement on the Kazakh test set (0.12%). However, when comparing BiLSTM with BiLSTM-CRF, the effectiveness of the CRF model is demonstrated. Therefore, it can be concluded that the soft attention mechanism does not necessarily improve model performance in word-level datasets. The differences between FEMSeg and FEMSeg-CRF and other models are further discussed in Section 4.6. This comparative experiment proved the effectiveness of the two methods proposed in this paper for the morphological segmentation task. In unsupervised segmentation, the morpheme boundaries were modeled using the correlation between n-grams. In supervised segmentation, words were fully represented through character-level feature learning and context feature learning and then input into the encoder of the Transformer model, where further attention was paid to the key features in the multidimensional space.