arXiv subjects

Adam Fourney

Publications and source records attributed to Adam Fourney.

22 records · Page 2Linked to original sources

arXiv2021

NL-EDIT: Correcting semantic parse errors through natural language interaction

We study semantic parsing in an interactive setting in which users correct errors with natural language feedback. We present NL-EDIT, a model for interpreting natural language feedback in the interaction context to generate a sequence of edits that can be applied to the initial parse to correct its errors. We show that NL-EDIT can boost the accuracy of existing text-to-SQL parsers by up to 20% with only one turn of correction. We analyze the limitations of the model and discuss directions for improvement and evaluation. The code and datasets used in this paper are publicly available at http://aka.ms/NLEdit.

cs.CL↗

arXiv2020

Conversations with Documents. An Exploration of Document-Centered Assistance

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 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.

cs.CL↗

arXiv2018

Analysis of Strategy and Spread of Russia-sponsored Content in the US in 2017

The Russia-based Internet Research Agency (IRA) carried out a broad information campaign in the U.S. before and after the 2016 presidential election. The organization created an expansive set of internet properties: web domains, Facebook pages, and Twitter bots, which received traffic via purchased Facebook ads, tweets, and search engines indexing their domains. We investigate the scope of IRA activities in 2017, joining data from Facebook and Twitter with logs from the Internet Explorer 11 and Edge browsers and the Bing.com search engine. The studies demonstrate both the ease with which malicious actors can harness social media and search engines for propaganda campaigns, and the ability to track and understand such activities by fusing content and activity resources from multiple internet services. We show how cross-platform analyses can provide an unprecedented lens on attempts to manipulate opinions and elections in democracies.

cs.SI↗

arXiv2018

Studying Preferences and Concerns about Information Disclosure in Email Notifications

The proliferation of network-connected devices and applications has resulted in people receiving dozens, or hundreds, of notifications per day. When people are in the presence of others, each notification poses some risk of accidental information disclosure; onlookers may see notifications appear above the lock screen of a mobile phone, on the periphery of a desktop or laptop display, or projected onscreen during a presentation. In this paper, we quantify the prevalence of these accidental disclosures in the context of email notifications, and we study people's relevant preferences and concerns. Our results are compiled from an exploratory retrospective survey of 131 respondents, and a separate contextual-labeling study in which 169 participants labeled 1,040 meeting-email pairs. We find that, for 53% of people, at least 1 in 10 email notifications poses an information disclosure risk. We also find that the real or perceived severity of these risks depend both on user characteristics and attributes of the meeting or email (e.g. the number of recipients or attendees). We conclude by exploring machine learning algorithms to predict people's comfort levels given an email notification and a context, then we present implications for the design of future contextually-relevant notification systems.

cs.HC↗