## 5 Conclusions  
We proposed a knowledge-grounded multi-turn chatbot model to effectively reflect newly-acquired external knowledge. To generate responses in the context of conversation history, it used an attention mechanism between a query and a context in the query-encoding step. To generate responses that reflect external knowledge, it used the query-knowledge mechanism in the knowledge encoding step. Furthermore, the model effectively mixed the two kinds of attention to consider the degree of association between the dialogue context and the given knowledge. In experiments with the Wizard-of-Wikipedia dataset, the proposed model showed better performances than the previous state-of-the-art models and generated responses more effectively, reflecting external knowledge. However, the proposed model has a limitation; that is, proper external knowledge should be given in advance. To overcome this, we will study an end-to-end knowledge-grounded chatbot model that searches external documents containing proper knowledge, summarizes the retrieved documents, and extracts proper knowledge from the summarized documents. As the future work, we will study a method to generalize an input utterance to a shallow semantic form that is a set of keywords, tense information, and modality information (e.g., \"I'll be able to go there.\" \(\rightarrow\) \"[[I, go, there], future, possibility]\"). Then, we will study a method to use the semantic form as an input of a Seq2 Seq model.  
Conceptualization, H.K.; methodology, H.K.; software, S.K.; validation, S.K.; formal analysis, H.K.; investigation, H.K.; resources, S.K.; data curation, S.K.; writing--original draft preparation, S.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. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Institute for Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (2019-0-00004, Development of semi-supervised learning language intelligence technology and Korean tutoring service for foreigners). This research was also supported by the MSIT (Ministry of Science, ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2020-2016-0-00465) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation).  
We especially thank the members of the NLP laboratory at Konkuk University for technical support.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multirow{2}{*}{**Knowledge**} & The first stable line-up consisted of [PERSON] (guitar, harmonica), [PERSON] (lead vocals), [PERSON] (guitar, backing vocals), [PERSON] (bass), [PERSON] (drums), and [PERSON] (piano). \\ \hline \multirow{4}{*}{**Context**} & I used to listen to the Rolling Stones a lot when I was a child. \\ \cline{2-3}  & Me too. I can’t believe they were formed in London as far back as 1962! What’s your favorite song? \\ \cline{2-3}  & I can’t even remember, to be honest! Do you know who the band members were? \\ \hline \multirow{2}{*}{**Gold Response**} & [PERSON] (of course), [PERSON], [PERSON], [PERSON], and I don’t remember who else. \\ \hline \multirow{2}{*}{**Dian-End2 End**} & I know, they were formed in London in 1962. \\ \hline \multirow{2}{*}{**Dian-TwoStage**} & I’m not sure, but I know that the band was formed in 1981 by drummer [PERSON] and vocalist [PERSON]. \\ \hline \multirow{2}{*}{**ITDD**} & I’m not sure, but I know they were formed in 1962. \\ \hline \multirow{2}{*}{**Proposed Model**} & [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON]. \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of proposed and previous models.