# UnifiedCut: A Simple and Efficient Neural Model for Thai, Burmese and Khmer Word Segmentation
## 4 Experiments
### Main Results  
#### 4.3.1 Word Segmentation for Thai  
Table 3 presents a performance comparison of several models on the BEST2010 and LST20 datasets for Thai word segmentation. PythaiNLP serves as a dictionary-based model that segments words using a maximum matching algorithm, providing a basic baseline for comparison. Sertis leverages bidirectional GRU networks, making it a neural model that captures the context in both directions. DeepCut, considered a state-of-the-art segmentation model, uses multiple CNN filters as feature encoders, with inputs that include both characters and their types. AttaCut-C is another CNN-based model that stands out for using dilated CNN filters, which is designed for faster processing due to its reduced number of CNN filters compared to DeepCut. Its variant, AttaCut-CS, further enhances segmentation by incorporating syllable embeddings as additional input. Finally, THDICTSDR takes a dictionary-based approach but introduces Sparse Distributed Representations (SDRs) to capture contextual information, improving its understanding of Thai text structure. Together, these models demonstrate a range of techniques and architectural choices that address the unique challenges of Thai word segmentation.  
The performance results in Table 3 highlight the strengths of UnifiedCut in Thai word segmentation, especially in comparison to other models. UnifiedCut consistently achieves top scores in precision, recall, F1-score, and OOV (out-of-vocabulary) handling, demonstrating its versatility and robustness. UnifiedCut shows the highest precision among all models with 0.971 on BEST2010 and 0.988 on LST20. UnifiedCut also performs well in the recall, although on the BEST2010 dataset, DeepCut slightly surpasses it. UnifiedCut's F1-score is the highest across both datasets (0.965 on BEST2010 and 0.988 on LST20), showcasing its strong balance between precision and recall. The results suggest that UnifiedCut is well rounded, effectively capturing correct segments from multiple n-gram inputs via a multi-head attention encoder.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Hyperparameter** & **Values** & **Description** \\ \hline n-grams & 1, 2, 3 & Multiple n-grams as sequence inputs \\ n-gram-embed-dim & 32 & Embedding size for the input n-grams \\ n-gram-embed-dropouts & 0.15 & Dropout rate for the n-gram embeddings \\ transformer-window-size & 21 & Sliding window size of the input sequences \\ transformer-layer-num & 1 & Layer number for the transformer encoder \\ transformer-head-num & 8 & Header number of the transformer encoder \\ transformer-feedforward-dim & 128 & Feedforward dense layer size \\ encoder-output-dim & 64 & Output dimension for the the context encoder \\ \hline optimizer & Adam & Optimizer for training the model \\ lr & 0.001 & Learning rate \\ max-epochs & 20 & Max epochs \\ \hline \hline \end{tabular}
\end{table}
Table 2: Default hyperparameters values of the UnifiedCut.  
UnifiedCut  demonstrates the best OOV handling, with OOV recall (\(R_{\text{OOV}}\)) scores of 0.703 on BEST2010 and 0.697 on LST20, outperforming all other models. A high \(R_{\text{OOV}}\) score means that UnifiedCut  is especially adept at handling words not seen in training data, which is a crucial advantage in real-world applications where new or rare words frequently appear.