Abstract: Multi-party Conversational Systems are systems with natural language interaction between one or more people or systems. From the moment that an utterance is sent to a group, to the moment that it is replied in the group by a member, several activities must be done by the system: utterance understanding, information search, reasoning, among others. In this paper we present the challenges of designing and building multi-party conversational systems, the state of the art, our proposed hybrid architecture using both rules and machine learning and some insights after implementing and evaluating one on the finance domain.

Introduction: Back to 42 BC, the philosopher Cicero has raised the issue that although there were many Oratory classes, there were none for Conversational skills BIBREF0 . He highlighted how important they were not only for politics, but also for educational purpose. Among other conversational norms, he claimed that people should be able to know when to talk in a conversation, what to talk depending on the subject of the conversation, and that they should not talk about themselves. Norms such as these may become social conventions and are not learnt at home or at school. Social conventions are dynamic and may change according to context, culture and language. In online communication, new commonsense practices are evolved faster and accepted as a norm BIBREF1 , BIBREF2 . There is not a discipline for that on elementary or high schools and there are few linguistics researchers doing research on this field. On the other hand, within the Artificial Intelligence area, some Conversational Systems have been created in the past decades since the test proposed by Alan Turing in 1950. The test consists of a machine's ability to exhibit intelligent behavior equivalent to, or indistinguishable from that of a human BIBREF3 . Turing proposed that a human evaluator would judge natural language conversations between a human and a machine that is designed to generate human-like responses. Since then, many systems have been created to pass the Turing's test. Some of them have won prizes, some not BIBREF4 . Although in this paper we do not focus on creating a solution that is able to build conversational systems that pass the Turing's test, we focus on NDS. From BIBREF5 , "NDS are systems that try to improve usability and user satisfaction by imitating human behavior". We refer to Conversational Systems as NDS, where the dialogues are expressed as natural language texts, either from artificial intelligent agents (a.k.a. bots) or from humans. That said, the current popular name to systems that have the ability to make a conversation with humans using natural language is Chatbot. Chatbots are typically used in conversational systems for various practical purposes, including customer service or information acquisition. Chatbots are becoming more widely used by social media software vendors. For example, Facebook recently announced that it would make Facebook Messenger (its 900-million-user messaging app by 2016), into a full-fledged platform that allows businesses to communicate with users via chatbots. Google is also building a new mobile-messaging service that uses artificial intelligence know-how and chatbot technology. In addition, according to the Wall Street Journal, there are more than 2 billion users of mobile apps. Still, people can be reluctant to install apps. So it is believed that social messaging can be a platform and chatbots may provide a new conversational interface for interacting with online services, as chatbots are easier to build and deploy than apps BIBREF6 . China seems to be the place where chatbots adoption and use is most advanced today. For example, China's popular WeChat messaging platform can take payments, scan QR codes, and integrate chatbot systems. WeChat integrates e-mail, chat, videocalls and sharing of large multimedia files. Users can book flights or hotels using a mixed, multimedia interaction with active bots. WeChat was first released in 2011 by Tecent, a Chinese online-gaming and social-media firm, and today more than 700 million people use it, being one of the most popular messaging apps in the world (The Economist 2016). WeChat has a mixture of real-live customer service agents and automated replies (Olson 2016). Still, current existing chatbot engines do not properly handle a group chat with many users and many chatbots. This makes the chatbots considerably less social, which is a problem since there is a strong demand of having social chatbots that are able to provide different kinds of services, from traveling packages to finance advisors. This happens because there is a lack of methods and tools to design and engineer the coordination and mediation among chatbots and humans, as we present in Sections 2 and 3. In this paper, we refer to conversational systems that are able to interact with one or more people or chatbots in a multi-party chat as MPCS. Altogether, this paper is not meant to advance the state of the art on the norms for MPCS. Instead, the main contributions of this paper are threefold: We then present some discussion and future work in the last section.

Question: What evaluation metrics did look at?
Answer: precision, recall, F1 and accuracy