# UnifiedCut: A Simple and Efficient Neural Model for Thai, Burmese and Khmer Word Segmentation
## 4 Experiments
### Main Results
#### 4.3.2 Word Segmentation Results for Burmese and Khmer  
To demonstrate the performance of the UnifiedCut  in other South Asian languages such as Burmese and Khmer, we also conducted a comparative analysis of the most advanced models in the benchmark datasets of both languages, respectively. BERT + BiLSTM-CRF + Fine-tune is the state-of-the-art Burmese word segmentation model. It uses the pre-trained Burmese BERT and BiLSTM-CRF as encoders to learn contextual Burmese word segmentation features. KhPOS is a Khmer word segmentation and POS-tagging system based on a deep learning network to remove the adverse dependency effect.  
The results in Table 4 indicate that UnifiedCut outperforms the benchmark models, BERT + BiLSTM-CRF for Burmese and KhPOS for Khmer, across all evaluation metrics, highlighting its superior accuracy and adaptability. In terms of precision, UnifiedCut achieves the highest scores, reaching 0.979 for Burmese and 0.983 for Khmer, reflecting its ability to minimize false positives and ensure the accurate segmentation of word boundaries. It also demonstrates leading recall scores, with 0.981 for Burmese and 0.987 for Khmer, indicating its strong capability to capture actual word boundaries. These high precision and recall values yield the highest F1-scores for both languages, achieving 0.980 for Burmese and 0.985 for Khmer, underscoring the model's balanced performance. Additionally, UnifiedCut excels in out-of-vocabulary (OOV) word handling, with \(R_{\text{OOV}}\) scores of 0.595 for Burmese and 0.613 for Khmer, significantly surpassing those of the baseline models. This robust OOV performance demonstrates UnifiedCut's generalizability to unseen words, which is a crucial advantage for real-world applications. Overall, these results confirm UnifiedCut's effectiveness in segmenting words in Burmese and Khmer, leveraging multiple n-grams and multi-head attention encoders to deliver consistent and high-quality segmentation.  
\begin{table}
\begin{tabular}{l l l c c c} \hline  & **Model** & **Precision** & **Recall** & **F1-Score** & \(\text{R}_{\text{OOV}}\) \\ \hline \multirow{4}{*}{**F1-Score**} & BERT + BiLSTM-CRF & 0.968 & 0.970 & 0.969 & 0.406 \\  & UnifiedCut & **0.979** & **0.981** & **0.980** & **0.595** \\ \hline \multirow{4}{*}{**F2-Score**} & KhPOS & 0.969 & 0.973 & 0.971 & 0.435 \\  & UnifiedCut & **0.983** & **0.987** & **0.985** & **0.613** \\ \hline \end{tabular}
\end{table}
Table 4: Word-level performance comparison in Burmese (my) and Khmer (km) datasets.  
\begin{table}
\begin{tabular}{l l l c c c} \hline  & **Model** & **Precision** & **Recall** & **F1-Score** & \(\text{R}_{\text{OOV}}\) \\ \hline \multirow{4}{*}{**F1-Score**} & PythiaNLP & 0.704 \(\pm\) 0.191 & 0.645 \(\pm\) 0.185 & 0.673 \(\pm\) 0.194 & – \\  & Sertis & 0.854 \(\pm\) 0.182 & 0.903 \(\pm\) 0.147 & 0.878 \(\pm\) 0.163 & – \\  & AttaCut-C & 0.882 \(\pm\) 0.172 & 0.912 \(\pm\) 0.143 & 0.897 \(\pm\) 0.161 & 0.519 \\  & AttaCut-SC & 0.913 \(\pm\) 0.151 & 0.925 \(\pm\) 0.134 & 0.919 \(\pm\) 0.141 & 0.641 \\  & DeepCut & 0.961 \(\pm\) 0.143 & 0.966 \(\pm\) 0.122 & 0.963 \(\pm\) 0.133 & 0.605 \\  & THDICTSDR & 0.943 \(\pm\) 0.127 & **0.969** \(\pm\) 0.126 & 0.956 \(\pm\) 0.126 & 0.674 \\  & UnifiedCut & **0.971** \(\pm\) 0.121 & 0.960 \(\pm\) 0.103 & **0.965** \(\pm\) 0.112 & **0.703** \\ \hline \multirow{4}{*}{**F2-Score**} & DeepCut & 0.967 \(\pm\) 0.028 & 0.971 \(\pm\) 0.029 & 0.969 \(\pm\) 0.027 & 0.641 \\  & THDICTSDR & 0.944 \(\pm\) 0.030 & 0.969 \(\pm\) 0.031 & 0.956 \(\pm\) 0.029 & 0.653 \\ \cline{1-1}  & UnifiedCut & **0.988** \(\pm\) 0.023 & **0.987** \(\pm\) 0.025 & **0.988** \(\pm\) 0.023 & **0.697** \\ \hline \end{tabular}
\end{table}
Table 3: Word-level performance comparison on BEST2010 and LST20.