Conversations with Documents. An Exploration of Document-Centered Assistance
Maartje ter Hoeve, Robert Sim, Elnaz Nouri, Adam Fourney, Maarten de, Rijke, Ryen W. White

TL;DR
This paper explores the potential of conversational assistants focused on document-centered tasks, including a survey of user needs, analysis of user queries, and initial machine learning models for question detection and answering.
Contribution
It provides a comprehensive survey of document-centered assistance, analyzes user query types, and introduces initial models for detecting and answering document-related questions.
Findings
Document-centered questions constitute the majority of user queries.
Initial models can accurately detect document-related questions.
Models show promise for effective document question answering.
Abstract
The role of conversational assistants has become more prevalent in helping people increase their productivity. Document-centered assistance, for example to help an individual quickly review a document, has seen less significant progress, even though it has the potential to tremendously increase a user's productivity. This type of document-centered assistance is the focus of this paper. Our contributions are three-fold: (1) We first present a survey to understand the space of document-centered assistance and the capabilities people expect in this scenario. (2) We investigate the types of queries that users will pose while seeking assistance with documents, and show that document-centered questions form the majority of these queries. (3) We present a set of initial machine learned models that show that (a) we can accurately detect document-centered questions, and (b) we can build…
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Taxonomy
TopicsTopic Modeling · AI in Service Interactions · Speech and dialogue systems
