## 4 Experiments
### Analysis of Experimental Results
#### 4.2.1 Dialogue Semantic Search (SS)  
This section demonstrates the effectiveness of dialogue SS. We removed the sentence that was least relevant to the question in order to configure the input in the maximum capacity of the model. This can reduce the information loss caused by truncation. Table 4 shows that a performance improvement of 0.33% over the baseline method is achieved when dialogue SS is applied. In addition, when dialogue SS was removed from our model, the performance degraded by 1.37%, i.e., from 90.05% to 88.68%. This reduction is due to truncation of the rear part of the dialogue history when its length is greater than the maximum token length that the model can receive.  
Table 5 shows the dialogue when dialogue SS is not used, and Table 6 shows the dialogue when dialogue SS is used. We compare these two tables to demonstrate the effectiveness of dialogue SS. Since the dialogue is longer than the model can deal with, the dialogue is truncated at the last utterance of the boy in Table 5. In this problem, our baseline ALBERT\({}_{xlarge}\) received the dialogue in Table 5 as input, not the entire dialogue. The model deduces the correct answer by referring to the part that is not truncated. Hence, it selects the answer as (c) by referring to the previous utterance of the father, i.e., \"butterflies flying around the zoo\". It cannot use the appropriate information because the information required to solve the problem has been truncated. Our baseline ALBERT\({}_{xlarge}\) selected the wrong answer in this problem. However, Table 6 consists of information that is highly relevant to the problem. In this problem, the model using dialogue SS received the dialogue in Table 6 that is relevant to the question. Therefore, the model can find the appropriate information to solve the problem. The model used the boy's utterance \"they're inside\" as well as the next utterance \"What was it made of? [Glass]\" to infer the correct answer as \"(a) inside a glass enclosure\". Dialogue SS can thus capture the information required to solve the problem while minimizing information loss. Therefore, we conclude that if the length of dialogue is longer than the model can deal with, dialogue SS can effectively improve the performance.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **DSS** & **CSS** & **CSQA CL** & **Accuracy (\%)** \\ \hline \multirow{6}{*}{ALBERT\({}_{xlarge}\)} & \(\surd\) & & & 88.50 \\  & & \(\surd\) & & 88.83 \\  & & \(\surd\) & & 87.26 \\  & \(\surd\) & \(\surd\) & & 89.07 \\  & \(\surd\) & \(\surd\) & & 87.90 \\  & \(\surd\) & \(\surd\) & 89.76 \\  & & \(\surd\) & \(\surd\) & 88.68 \\ \hline DQACR (ALBERT\({}_{xlarge}\)) & \(\surd\) & \(\surd\) & \(\surd\) & 90.05 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Model performance for DREAM. The base model employs ALBERT\({}_{xlarge}\). The modules used are denoted by \(\surd\) in each line. DSS stands for dialogue Semantic Search, CSS stands for ConceptNet Semantic Search, and CSQA CL stands for CommonsenseQA Continual Learning. We achieved a performance improvement of 1.5% over the baseline method.  
\begin{table}
\begin{tabular}{l} \hline \hline
**Dialogue** \\ Father: [PERSON]. Time for bed [Why?] Why? It’s getting dark out. Well, do you want to talk before you go to bed? [Yeah] \\ Uh, what do you want to talk about? \\ Boy: Um, the zoo. \\ \hline Father: Uhh, okay. Then, you saw some butterflies, did not you? [Yeah] What colors were they? \\ Boy: After the bird show. \\ Father: After the bird show you saw them. Furthermore, were the butterflies flying around all over the zoo? \\ Boy: Uh, um, they’re \(\leftarrow\) truncate \\ \hline \hline \end{tabular}
\end{table}
Table 5: Dialogue history without applying dialogue SS. Because the latter part is truncated, the model cannot use the related information during the inference process. This adversely affects the inference of the model.