## 5 Conclusions and Future Works  
Dialogue-based multiple-choice QA tasks using existing PTLMs bear the following disadvantages: (1) Limited length of dialogue history that can be entered as input and (2) insufficient ability to perform commonsense reasoning. Through Semantic Search, we improved truncation-related problems by employing only relevant sentences as input. Moreover, we improved commonsense reasoning using CommonsenseQA [8] continual learning and ConceptNet [9] Semantic Search. Thus, we achieved a performance improvement of approximately 1.5% over the baseline method. In addition, our model contributes toward not only dialogue-based QA tasks but also QA datasets for future tasks, such as RACE [1] and SQuAD [2].  
However, our model has the following drawbacks: (1) It is overly dependent on Semantic Search results. If a ConceptNet sentence found through Semantic Search does not help solve the problem, it can reduce the amount of dialogue history used by the model and thus degrade the overall performance. (2) Although Semantic Search reduces information loss, some loss still occurs because of the truncated dialogue history. Therefore, in the future, we will carry out a study to improve these drawbacks. In total, 66% of the problems in the DREAM dataset can be solved without commonsense. Employing ConceptNet SS for such a problem rather reduces the length of dialogue that the model can refer to for solving the problem. The sole application of ConceptNet SS to problems requiring commonsense reasoning will further improve the performance. Thus, we will investigate a method in which the problem can be solved more efficiently by combining it with a classifier that determines whether the problem requires commonsense or not. Therefore, we will study the method used for the LM to solve the problem more efficiently by combining a classifier that distinguishes whether the problem requires commonsense or not.  
Conceptualization, software, investigation, methodology, visualization, writing--review and editing--S.J. and D.O.; investigation, visualization, writing--original draft--K.P.; validation, supervision, resources, project administration, and funding acquisition, H.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2021R1A6A1A03045425). In addition, it was also supported by the MSIT (Ministry of Science and ICT), Korea, under the ICT Creative Consilience program (IITP-2022-2020-0-01819) supervised by the IITP (Institute for Information & communications Technology Planning & Evaluation).  
Not applicable.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Model** & **Acc** \\ \hline Vanila ALBERT\({}_{xlarge}\) & 88.50 \\ DQACR (ALBERT\({}_{xlarge}\)) & 90.05 \\ \hline Vanila RoBERTa\({}_{large}\) & 84.66 \\ DQACR (RoBERTa\({}_{large}\)) & 85.64 \\ \hline \hline \end{tabular}
\end{table}
Table 7: DREAM performance on ALBERT\({}_{xlarge}\) and RoBERTa\({}_{large}\).  
Data Availability Statement: DREAM: [[https://dataset.org/dream/](https://dataset.org/dream/)]([https://dataset.org/dream/](https://dataset.org/dream/)), accessed on 1 March 2022, CommonsenseQA: [[https://www.tau-nlp.org/commonsenseqa](https://www.tau-nlp.org/commonsenseqa)]([https://www.tau-nlp.org/commonsenseqa](https://www.tau-nlp.org/commonsenseqa)), accessed on 1 March 2022, ConceptNet: https://conceptnetio/, accessed on 1 March 2022.  
The authors declare no conflict of interest.