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To predict the next most likely participant to interact in a multi-partyconversation is a difficult problem. In a text-based chat group, the onlyinformation available is the sender, the content of the text and the dialoguehistory. In this paper we present our study on how these information can beused on the prediction task through a corpus and architecture that integratesturn-taking classifiers based on Maximum Likelihood Expectation (MLE),Convolutional Neural Networks (CNN) and Finite State Automata (FSA). The corpusis a synthetic adaptation of the Multi-Domain Wizard-of-Oz dataset (MultiWOZ)to a multiple travel service-based bots scenario with dialogue errors and wascreated to simulate user's interaction and evaluate the architecture. Wepresent experimental results which show that the CNN approach achieves betterperformance than the baseline with an accuracy of 92.34%, but the integratedsolution with MLE, CNN and FSA achieves performance even better, with 95.65%.

A Hybrid Solution to Learn Turn-Taking in Multi-Party Service-based Chat Groups