## 5 Conclusions  
We proposed a generative, multi-turn chatbot model. To generate responses that consider dialogue histories, the proposed model used the query-context attention mechanism in the query encoding step. Furthermore, to improve the quality of responses, the proposed model used two types of WGAN: A WGAN for dialogue modeling and a WGAN for response generation. In experiments with DailyDialog datasets, the proposed model outperformed the previous state-of-the-art models and generated responses with higher quality. The proposed chatbot model has the lack of a consistent personality because it is typically trained using many dialogues from different speakers. To alleviate this problem, we will study how to have a chatbot that maintains a consistent persona. In addition, we will study how a chatbot can search and use outer knowledge for open-domain dialogue. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**BLEU**} & \multicolumn{3}{c}{**BOW Embedding**} & \multicolumn{3}{c}{**Intra-dist**} & \multicolumn{3}{c}{**Inter-dist**} \\ \cline{2-11}  & **R** & **P** & **F1** & **A** & **E** & **G** & **Dist-1** & **Dist-2** & **Dist-1** & **Dist-2** \\ \hline WAE & 0.341 & 0.278 & 0.306 & 0.948 & 0.578 & 0.846 & 0.830 & 0.940 & 0.327 & 0.583 \\ \hline WAE + QE & 0.442 & 0.268 & 0.334 & 0.947 & 0.680 & 0.845 & **0.913** & **0.995** & 0.322 & 0.475 \\ \hline WAE + QE + RR & **0.463** & **0.283** & **0.351** & **0.949** & **0.688** & **0.851** & 0.902 & 0.993 & **0.371** & **0.585** \\ \hline \hline \end{tabular}  
* P: Precision, R: Recall, F1: F1 score, A: Average, E: Extrema, G: Greedy. BLEU: bilingual evaluation understudy; BOW: bag-of-words; WAE: wasserstein auto-encoder; QE: query encoder; RR: response-to-response. The bolts indicate the highest scores in each evaluation measure.  
\end{table} 
Table 2: Performance comparison based on change in architecture. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**BLEU**} & \multicolumn{3}{c}{**BOW Embedding**} & \multicolumn{3}{c}{**Intra-dist**} & \multicolumn{3}{c}{**Inter-dist**} \\ \cline{2-11}  & **R** & **P** & **F1** & **A** & **E** & **G** & **Dist-1** & **Dist-2** & **Dist-1** & **Dist-2** \\ \hline HRED & 0.232 & 0.232 & 0.232 & 0.915 & 0.511 & 0.798 & 0.935 & 0.969 & 0.093 & 0.097 \\ \hline CVAE & 0.265 & 0.222 & 0.242 & 0.923 & 0.543 & 0.811 & 0.938 & 0.973 & 0.177 & 0.222 \\ \hline CVAE-BOW & 0.256 & 0.224 & 0.239 & 0.923 & 0.540 & 0.812 & **0.947** & 0.976 & 0.165 & 0.206 \\ \hline CVAE-CO & 0.259 & 0.244 & 0.251 & 0.914 & 0.530 & 0.818 & 0.821 & 0.911 & 0.106 & 0.126 \\ \hline WAE & 0.341 & 0.278 & 0.306 & 0.948 & 0.578 & 0.846 & 0.830 & 0.940 & 0.327 & 0.583 \\ \hline WAE + QE + RR & **0.463** & **0.283** & **0.351** & **0.949** & **0.688** & **0.851** & 0.902 & **0.993** & **0.371** & **0.585** \\ \hline \hline \end{tabular}  
* The bolts indicate the highest scores in each evaluation measure.  
\end{table} 
Table 3: Performance comparison between proposed and previous models. **Author Contributions:** Conceptualization, H.K. and J.K.; methodology, J.K.; software, J.K.; validation, J.K.; and S.O. ; formal analysis, J.K.; investigation, J.K.; resources, S.O. ; data curation, S.O. ; writing--original draft preparation, J.K.; writing--review and editing, H.K. ; visualization, H.K. ; supervision, H.K. and O.-W.K. ; project administration, H.K. and O.-W.K. ; funding acquisition, H.K. and O.-W.K. **Funding:** This work was supported by the Institute of Information and Communications Technology Planning and Evaluation (IITP), grant funded by the Korean government (MSIT) (2019-0-0004, Development of semi-supervised learning language intelligence technology and Korean tutoring service for foreigners), and was partially supported by the National Research Foundation of Korea (NRF), grant funded by the Korean government (MSIP) (No.2016R1A2B4007732). This work was also partially supported by the Hyundai motor group.