Abstract: Many businesses and consumers are extending the capabilities of voice-based services such as Amazon Alexa, Google Home, Microsoft Cortana, and Apple Siri to create custom voice experiences (also known as skills). As the number of these experiences increases, a key problem is the discovery of skills that can be used to address a user's request. In this paper, we focus on conversational skill discovery and present a conversational agent which engages in a dialog with users to help them find the skills that fulfill their needs. To this end, we start with a rule-based agent and improve it by using reinforcement learning. In this way, we enable the agent to adapt to different user attributes and conversational styles as it interacts with users. We evaluate our approach in a real production setting by deploying the agent to interact with real users, and show the effectiveness of the conversational agent in helping users find the skills that serve their request.

Introduction: Modern speech-based assistants, such as Amazon Alexa, Google Home, Microsoft Cortana, and Apple Siri, enable users to complete daily tasks such as shopping, setting reminders, and playing games using voice commands. Such human-like interfaces create a rich experience for users by enabling them to complete many tasks hands- and eyes-free in a conversational manner. Furthermore, these services offer tools to enable developers and customers to create custom voice experiences (skills) and as a result extend the capabilities of the assistant. Amazon's Alexa Skills Kit BIBREF0, Google's Actions and Microsoft's Cortana Skills Kit are examples of such tools. As the number of skills (with potentially overlapping functionality) increases, it becomes more difficult for end users to find the skills that can address their request. To mitigate the skill discovery problem, recently researchers have proposed solutions for personalized domain selection and continuous domain adaptation in speech-based assistants BIBREF1, BIBREF2. Although such solutions help users find skills, in scenarios such as searching for a game where many different skills exist and user's preferences change, routing the user to a particular experience would not be satisfactory. In such cases, the assistant should initiate a conversation with the user, making recommendations, asking for preferences, and allowing the user to browse through different options. Similar to other search problems, personalization is important for conversational skill discovery and can be achieved at two levels: 1) personalization of skill recommendations, and 2) personalization of the interaction. Users have evolving attributes (e.g., first-time vs returning user) and different conversational styles and preferences (e.g., brief vs verbose communication) which affect how they respond to what the agent is proposing and its recommendations. By personalizing the interaction according to user attributes, conversational styles and preferences, the speech-based assistant can help speed up the conversation process BIBREF3 and increase user satisfaction. However, existing works are limited with respect to considering user's evolving attributes and diverse multi-aspect preferences BIBREF4, such as preferences with respect to how the conversational agent interacts with them. In this paper, we focus on conversational discovery of skills to guide customers from an intent to a specific skill or set of skills that can serve their request. To this end, we start with a rule-based agent and improve it by using reinforcement learning (RL), enabling the agent to adapt to different conversational styles as it interacts with users. In summary, the contributions of this paper are as follows: 1) We introduce the problem of conversational skill discovery for large-scale virtual assistants. 2) We describe a solution which enables the assistant to adapt to user's attributes (e.g., first-time user vs returning user) and conversational styles (e.g., brief vs. verbose). 3) We conduct experiments in a real production setting by deploying the agent to interact with real users in large scale, showing that the personalized policy learned using RL significantly outperforms a one-fits-all rule-based agent in terms of success rate (measured in terms of number of dialogs which result in launching a skill) with significantly shorter dialogs.

Question: Which of the two ensembles yields the best performance?
Answer: Answer with content missing: (Table 2) CONCAT ensemble