## 3 Results
### Experiments on the Multi-Hot Language Model  
The performance of the multi-hot LM that maintained the morpheme units proposed in this paper was evaluated with regard to natural language processing and compared with the existing models. The training and evaluation of the test domains were conducted \begin{table}
\begin{tabular}{c c c} \hline \hline
**Loss Function** & **Exact** & **F1-Score** \\ \hline Softmax Cross Entropy & 69.8\% & 76.3\% \\ Binary Cross Entropy & 43.3\% & 55.8\% \\ Multi-hot Softmax Cross Entropy & **73.6\%** & **80.1\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results for KorQuAD v1.0 regarding the loss function; list of tokens based on BPE and syllables, tested with a small model. Figure 2: Experimental area (KorQuAD v1.0, NER, SRL, NSMC) usage model. within the same hardware environment. The same training settings were used for testing models of the same size. In Table 3, the evaluation results of the model proposed in this paper and the existing models for each domain are presented by token configuration and model size. Among the four domains, KorQuAD v1.0--which was used to test machine reading comprehension--showed lower performance than the existing BPE base. The remaining three domains showed higher performance, excluding SRL in the small models. A detailed analysis of each domain was conducted to determine the cause of the differences from the existing model. First, a detailed evaluation of the correct answers was conducted on the test results from KorQuAD v1.0. Subsequently, an analysis of the performance differences with existing models was conducted. In addition, the results were divided and analyzed in relation to the exact match ratio according to the answer form. The test consisted of the case where at least one morpheme was separated and the case where no morphemes were separated when the morpheme of the correct answer was tokenized. The analysis results were extracted from the small models. In Table 4, the comparison showed a large number of correct answers for unseparated morphemes in the existing BPE model but lower performance than the dictionary-based syllable token combinations when separation occurred. The table shows that maintaining morphological forms has a significant effect on performance. The small models performed well in NER but not in SRL. However, the multi-hot LM showed high performance in both domains for the basic models. Proper nouns, which frequently appear as answers in NER, appear less frequently in corpora and show more diverse combinations than does commonly used vocabulary. Therefore, the multi-hot LM showed high performance as morphemes are often separated or cannot be tokenized in the existing BPE base. The following table shows the F1-score results of 3 main tags from a total of 15 tags in the NER for each model. The selected tags were person, location, and organization. These are typical categories of named entities that are in the form of proper nouns. The performance comparison for these tags was based on three small models. Table 5 shows higher performance for the set of named entities with a high number of proper nouns in the model that is represented as a set of syllables than in the existing model. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Token List**} & \multicolumn{4}{c}{**KorQuAD Exact Match Ratio**} \\ \cline{2-5}  & \multicolumn{2}{c}{**Not Divided**} & \multicolumn{2}{c}{**Divided**} \\ \hline BEP base & **2046/2618** & **78.15\%** & 2301/3156 & 72.90\% \\ BPE + Syllable & 1958/2618 & 74.78\% & 2291/3156 & 72.59\% \\ BPE + Dictionary & 2002/2618 & 76.47\% & **2326/3156** & **73.70\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ratio of exact matches based on the separation of morphemes of KorQuAD v1.0 in the small model.