Turing AI Fellowship:Neural Conversational Information Seeking Assistant

There have been significant recent advances in Virtual Personal Assistants (VPAs) such as Google Assistant, Siri, and Alexa. However, development of these assistant systems is expensive and difficult (often requiring multiple skilled PhDs). And further, current systems are capable of limited "conversations", with most actions consisting of a single interaction in limited domains to perform simple tasks ("set a timer", "play music", etc...). The goal of this research is to develop research to enable a future conversational search systems that can help solve complex information tasks. Examples of these types of information tasks could be "Teach me about the causes of climate change." or "Help me write the literature survey for this paper." These require complex discussion and long-running modelling of the user and their information task. We propose building on recent advances in machine learning to adapt a general purpose information agent for specialized domains (like health, law, finance) by "machine reading" of text (such documents from a website) to learn a domain model and to discover information tasks automatically from existing interaction data such as search logs, existing conversations, or help tickets. The result of this work will be information agents that can effectively work with the user (including asking questions back and forth) and explain their reasoning more effectively than current information assistants.