Conversational intelligent agents are software agents that imitate human conversation – spoken, written, or both. Conversational intelligent systems such as Siri, Alexa and others have impacted how humans interact with computers in daily life. Studies of interactions between humans and conversational agents have found that increasing naturalness of interactions promote warmer attitudes and richer language used in their conversations (Novielli, Fiorella de Rosis, & Mazzotta, 2010). Many practical applications for conversational intelligent agents have been proposed, for example, for companionship to improve mental well-being and for therapy.
Categories of conversational intelligent agents
General chatbots and conversational agents
Conversational agents have been generally categorized into two main categories, namely task-oriented and general chat. Chatbots are traditionally aimed primarily at small talk, while task-oriented models are designed to carry out information-oriented and transactional tasks (Thomas et al., 2018). Initiatives such as Alexa Prize conversational AI challenge intend to push the boundaries of conversational AI to develop more intelligent chatbots to carry on in-depth conversations about a number of topics, not just small talk.
General recommender systems
Recommendation systems have been studied for decades, and are now pervasive (Ricci, Rokach & Shapira, 2015). Traditional recommendation algorithms have been classified as primarily model-based or content-based, where a classifier model is trained for each user’s profile, and collaborative filtering, where a user’s unknown preferences are estimated based on the neighborhood of similar users. More effective methods have been shown to be a hybrid of the two approaches with increasingly sophisticated methods reported for movie and news recommendation.
Utterance suggestion in conversational agents
Yan and Zhao (2018) describe an end-to-end generative model, which given a user query, generates a response, and a proactive suggestion to continue the conversation. However, generative models like this still strictly rely on training corpora or restricted information, without the ability to query external data sources, thus limiting their capacity for an informative conversation. In the other work, Yan et al. (2017) describe a next-utterance suggestion approach for retrieving utterances from a conversational dataset to use as suggestions, along with the response. The proposed model learns to give suggestions related to the response, to continue the conversation on the same topic.
Conversational recommendation
Recently, the idea of conversational recommendation was introduced (Christakopoulou et al., 2016), primarily as a way to elicit the user’s interests for item recommendation. For example, Sun and Zhang (2018) introduced an end-to-end reinforcement learning framework for a personalized conversational sales bot, and in Li et al. (2018), a combination of deep learning-based models is used for conversational movie recommendation. Currently, most existing conversational agents are designed for a single domain, such as Movies or Music. An open-domain conversational agent that coherently and engagingly converses with humans on a variety of topics, remains an aspirational goal for dialogue systems (Venkatesh et al., 2018).