Conversations with Documents. An Exploration of DocumentCentered Assistance. The role of conversational assistants has become more prevalent in helping people increase their productivity. Documentcentered 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 useru0027s productivity. This type of documentcentered assistance is the focus of this paper. Our contributions are threefold: (1) We first present a survey to understand the space of documentcentered 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 documentcentered 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 documentcentered questions, and (b) we can build reasonably accurate models for answering such questions. These positive results are encouraging, and suggest that even greater results may be attained with continued study of this interesting and novel problem space. Our findings have implications for the design of intelligent systems to support task completion via natural interactions with documents.

QRFA: A DataDriven Model of InformationSeeking Dialogues. Understanding the structure of interaction processes helps us to improve informationseeking dialogue systems. Analyzing an interaction process boils down to discovering patterns in sequences of alternating utterances exchanged between a user and an agent. Process mining techniques have been successfully applied to analyze structured event logs, discovering the underlying process models or evaluating whether the observed behavior is in conformance with the known process. In this paper, we apply process mining techniques to discover patterns in conversational transcripts and extract a new model of informationseeking dialogues, QRFA, for Query, Request, Feedback, Answer. Our results are grounded in an empirical evaluation across multiple conversational datasets from different domains, which was never attempted before. We show that the QRFA model better reflects conversation flows observed in real informationseeking conversations than models proposed previously. Moreover, QRFA allows us to identify malfunctioning in dialogue system transcripts as deviations from the expected conversation flow described by the model via conformance analysis.