## 6 Conclusions and Future Work  
This research paper introduces and presents the results of a successful algorithm designed for the purpose of topic and subtopic detection in open-domain dialogues using scalable Zero-Shot approaches. Additionally, a methodology was proposed to automatically identify inconsistent responses in generative-based chatbots utilizing automatic clustering techniques. The experimental results demonstrate the efficacy of the topic detection algorithm, achieving an F1 weighted score of 0.66 when detecting 13 distinct topics and an F1 weighted score of 0.45 when detecting 18 distinct topics (Table 1). In terms of the subtopic level, a weighted F1 score of 0.67 was achieved. Moreover, the algorithm exhibits precise estimation capabilities in determining the number of diverse responses, as evidenced by an MSE of 3.4 calculated over a set of 109 handcrafted responses, 15 sets of original questions plus their paraphrases, passed to 4 small model chatbots. In the case of the 120 questions created with GPT-4, 15 question sets each consisting of 1 original and its respective 7 paraphrases, and fed into 4 State-of-the-Art chatbots, the overall resulting MSE was 3.2. Our forthcoming research will primarily focus on two main aspects: expanding the range of high-level topics and subsequently evaluating the algorithm's performance in identifying subtopics. Additionally, we aim to include this topic and subtopic classifier in the dialogue management for the chatbot that we are using during our participation in the Alexa Socialbot Grand Challenge (SGC5). Regarding the detection of inconsistent responses, our efforts will be directed towards the development of controllable algorithms and architectures, such as TransferTransfo [36] or CTRL [50], leveraging persona profiles \begin{table}
\begin{tabular}{c c c} \hline
**Dataset** & **\#Labels** & **Average Time (ms)** \\ \hline SGC5 & 11 & 27 \\ DailyDialog Topic & 18 & 39 \\ DailyDialog Subtopic & 6 & 19 \\ \hline \end{tabular}
\end{table} 
Table 13: Latencies of the BART classifier calculating topics and subtopics for SGC5 and DailyDialog datasets. \begin{table}
\begin{tabular}{c c} \hline
**Dataset** & **Average Time (s)** \\ \hline BlenderBot3 (3B) & 4.93 \(\pm\) 0.05 \\ ChatGLM & 4.29 \(\pm\) 0.03 \\ DialoGPT-large & 4.41 \(\pm\) 0.08 \\ GPT-4 & 4.40 \(\pm\) 0.05 \\ \hline \end{tabular}
\end{table} 
Table 14: K-Means and Silhouette clustering latencies for the 4 models used. within these frameworks with the idea of generating more consistent responses. Furthermore, we seek to explore mechanisms to incorporate these identified inconsistencies into an automated evaluation of dialogue systems [51; 52], according to the recommendations made in [20]. Conceptualization, M.R.-C., M.E.-G., L.F.D., F.M. and R.C. ; Methodology, M.R.-C., M.E.-G. and L.F.D. ; Software, M.R.-C. and M.E.-G.; Validation, M.R.-C. and M.E.-G.; Formal analysis, M.R.-C., M.E.-G., L.F.D., F.M. and R.C. ; Investigation, M.R.-C., M.E.-G., L.F.D., F.M. and R.C. ; Resources, M.R.-C. and M.E.-G.; Data curation, M.R.-C.; Writing--original draft, M.R.-C.; Writing--review & editing, M.R.-C., M.E.-G., L.F.D., F.M. and R.C. ; Visualization, M.R.-C. and M.E.-G.; Supervision, M.R.-C., M.E.-G., L.F.D., F.M. and R.C. ; Project administration, M.R.-C. and L.F.D. ; Funding acquisition, L.F.D. All authors have read and agreed to the published version of the manuscript. This work is supported by the European Commission through Project ASTOUND (101071191--HORIZON-EIC-2021-PATHFINDERCHALLENGES-01). Data Availability Statement: Data sharing not applicable. This work is supported by project BEWORD (PID2021-126061 OB-C43) funded by MCIN/AEI/10.13039/501100011033 and, as appropriate, by \"ERDF A way of making Europe\", by the \"European Union\". In addition, it is part of the R&D project \"Cognitive Personal Assistance for Social Environments (ACOGES)\", reference PID2020-113096 RB-I00, funded by MCIN/AEI/10.13039/501100011033.