# Better Word Representation Vectors Using Syllabic Alphabet: A Case Study of Swahili
## 6 Results and Discussion
### Our Model Results (WEFSE)  
We present the results of our syllable-aware language model in Table 6. The experiments were carried out on Swahili, Xhosa and Shona, which are agglutinative and syllabic-based languages.  
We observed unexpected results on the Shona and Xhosa datasets where the character model slightly outperforms the syllable model. We attribute these results to the limited context of the datasets and the fact that Swahili is morphologically richer than Xhosa and Shona [(60)]. For Swahili, the syllable model achieves better results than the character model on both the small and medium datasets. The perplexity values achieved by this model compete favorably with state-of-the-art models in NLP. The results on the medium dataset allude to the fact that rare words are well handled when the word embeddings are composed from syllable embeddings. Indeed, the model represents the words better because the syllabic alphabet supplies the constituent syllables of any word for the model to learn from. Therefore, these results validate the quality of WEFSE and our hypothesis.  
It should be noted that the syllable vocabulary sizes are different because we used unique syllabic alphabet for Swahili, Xhosa and Shona. Xhosa has a large syllabic vocabulary size that employs the entire English alphabet. During processing, we added two characters to encapsulate each word, padding character, and start and end of sentence markers to the syllable dictionary. This explains the size of Swahili vocabulary.  
Further, we investigated the effect of different character and syllable embedding dimensions on the perplexity values. Figure 3 shows that there is no statistically significant effect of the dimension on perplexity values. However, the most appropriate embedding dimensions for our experiment could have been 200 or 500 for both characters and syllables; we used a dimension of 200.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Character** & \multicolumn{3}{c}{**Syllable**} \\ \hline  & Size & PPL & Size & PPL \\ Xhosa & 37 & 114.127 & 272 & 114.618 \\ Shona & 33 & 110.167 & 235 & 110.242 \\ Swahili (small) & 33 & 31.576 & 213 & **31.229** \\ Swahili (medium) & 33 & 47.893 & 219 & **45.859** \\ \hline \hline \end{tabular}
\end{table}
Table 6: The perplexity results (PPL) of the WEFSE model. The Syllabic model completely outperforms the character counterpart on both small and medium datasets. The bolded values represent the best performance.  
Figure 3: The effect of different character and syllable dimensions on perplexity values. The most appropriate dimension for our experiment is 200 or 500.  
Communication is critical in every sector of a community, society, region, country or continent. This is because communication supports every field including health, education, trade, engineering, industry, transport, service, hospitality and games. Therefore, automated information systems which are facilitated by NLP play an important role in economic development of a region. We are of the opinion that our WEFSE will inspire development of such systems for East and Central Africa to foster and enhance regional integration and development.