[introductory/intermediate] Neural Approaches to Conversational Information Retrieval
A conversational information retrieval (CIR) system is an information retrieval (IR) system with a conversational interface which allows users to interact with the system to seek information via multi-turn conversations of natural language (in spoken or written form). This tutorial surveys recent advances in CIR, focusing on neural approaches that have been developed in the last few years. We present (1) a typical architecture of a CIR system, (2) new tasks and applications which arise from the needs of developing such a system, in comparison with traditional keyword-based IR systems, (3) new methods of conversational question answering, and (4) case studies of several CIR systems developed in research communities and industry.
This tutorial will include four-hour materials scheduled as in the following six parts:
Part 1: Introduction to Conversational Information Retrieval (CIR)
This part motivates the research of CIR by reviewing the studies on how people search and how information seeking can be cast as human-machine conversations.
Part 2: Conversational Search Systems
This part discusses the core components of CIR systems, focusing on the new challenges raised from the conversational nature in comparison with classic, keyword-based, IR systems. There are three sections in this part. The first section describes the general framework of a CIR system; the remaining two present two core CIR components, conversational query understanding and conversational document ranking, respectively.
Part 3: Interactive Information Retrieval Methods
This part presents a set of methods that aim to make CIR systems play a more active role in human-machine interactions of information seeking. These methods collectively allow a CIR system to actively help a user clarify her search intent by asking clarification questions, guide the user to discover and learn new knowledge by suggesting and recommending related information and search in new directions, and synthesize retrieved content to form a new understanding. Finally, we present the challenges and solutions when evaluating such an interactive CIR system.
Part 4: Conversational QA over Texts
This part presents recent developments in conversational Question Answering (QA) systems that allow users to query a document collection in natural language. Such text-QA agents are much easier to use in mobile devices than traditional search engines in that they provide concise, direct answers to user queries, as opposed to a list of document links.
Part 5: Conversational QA over Structured Databases
This part presents recent developments in conversational Question Answering over structured databases, i.e. Knowledge Bases (KBs).
Part 6: Case Studies of Commercial Systems
The last part of this tutorial reviews a variety of commercial systems for CIR and related tasks. Due to the proprietary nature of many of these systems, we limit our review to summarizing published material about the systems.
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Jianfeng Gao is a Distinguished Scientist and Vice President of Microsoft. He is the head of the Deep Learning group at Microsoft Research, leading the development of AI systems for natural language processing, Web search, vision language understanding, dialogue, and business applications. He is an affiliate professor of Computer Science & Engineering at University of Washington, an IEEE Fellow, and a Distinguished Member of ACM.