# Better Word Representation Vectors Using Syllabic Alphabet: A Case Study of Swahili
## 6 Results and Discussion
### Baseline Results  
We performed two baseline experiments to assess the quality of WEFSE. [PERSON] et al. [21] composed word embeddings from syllable-like units using sum, concatenation (concat) and convolutional neural network (cnn) models. These models provided us with a platform to assess our word embeddings (WEFSE) by comparing the quality of word embeddings generated from finite syllables and syllable-like units. Therefore, we ran the small Swahili dataset on the models using syllables from both the syllabic alphabet and an external hyphenator. The results of these experiments are reported in Table 4 where the LSTM-syl (cnn) model outperforms the LSTM-syl (sum) and LSTM-syl (concat) models for both the syllables from the hyphenator and our syllables. This is attributed to the ability of convolutional neural networks to extract quality syllable features that are used to compose the word embeddings. However, the syllable-like units outperform our syllables across the models though the results are competitive to state-of-art models. Note the number of unique syllables in the scenarios; the hyphenator generates **18,440** unique syllables compared to **210** from our syllabic alphabet for the same dataset. This demonstrates that syllable-like units are random combinations of characters that do not carry any semantic meaning. According to [PERSON] et al. [29], quality embeddingsare composed from meaningful sub-word units. We resolved to use the CNN for our model based on this baseline experiment. The work of [PERSON] and [PERSON] [25], in which words, characters, character trigrams and morphemes are used to compose word embeddings using bi-directional LSTM and add functions, offered another benchmark for our WFSE. However, we did not experiment on the morphemes or the add function because of the need for an external segmenter and the earlier baseline experiment which had used sum function. The objective was to assess our WFSE on a bi-directional LSTM as a composition function and compare the performance of syllables to characters and trigrams. Again, we carried out the experiments using the small Swahili dataset and the results are outlined in Table 4. The syllable model outperforms the character-aware counterpart negating the conclusion by [PERSON] et al. [21] that characters are superior to syllables. However, the trigrams outperform both character-aware and syllable-aware models, even though they possess no semantic meaning. Generally, the two baselines experiments demonstrated the superiority of CNNs over bi-directional LSTMs in the extraction of quality features for NLP tasks. However, a compositional model combining the two functions is worth exploring and we leave it for future consideration. Generally, we argue that our model is better than the baselines in representing Swahili words. This is because we are convinced that proper representation of Swahili words should effectively cater for its agglutinative features, especially in the verbs that may consist of subject, tense, relative, root and some extensions, which are themselves syllables. This implies that a good compositional model for Swahili should split the words into sub-word units without distorting the agglutinative components in terms of orthography and position. Table 5 provides a comparative analysis on how the compositional models were splitting Swahili words. It clearly demonstrates that syllable models maintain the agglutinative components. We therefore expect superior word representation vectors from syllables as compared to characters and trigrams.