eAssistant: Cognitive Assistance for Identification and Auto-Triage of Actionable Conversations

TitleeAssistant: Cognitive Assistance for Identification and Auto-Triage of Actionable Conversations
Publication TypeConference Paper
Year of Publication2017
AuthorsHamid R. Motahar Nezhad, Kalpa Gunaratna, Juan Cappi
Conference Name26th International World Wide Web Conference (WWW 2017)
Date Published04/03/2017
Conference LocationPerth, Australia
KeywordsActivity Management, Cognitive Assistance, information extraction, Natural Language Interface, Natural Language Understanding, Online Learning, personalization
Abstract

The browser and screen have been the main user interfaces of the Web and mobile apps. The notification mechanism is an evolution in the user interaction paradigm by keeping users updated without checking applications. Conversational agents are posed to be the next revolution in user interaction paradigms. However, without intelligence on the triage of content served by the interaction and content differentiation in applications, interaction paradigms may still place the burden of information overload on users. In this paper, we focus on the problem of intelligent identification of actionable information in the content served by applications, and in particular in productivity applications (such as email, chat, messaging, social collaboration tools, etc.). We present eAssistant, which offers a novel fine-grained action identification method in an adaptive, personalizable, and online-trainable manner, and a cognitive agent/API that uses action information and user-centric conv ersation characteristics to auto-triage user conversations. The introduced method identifies individual actions and associated metadata; it is extensible in terms of the number of action classes; it learns in an online and continuous manner via user interactions and feedback, and it is personalizable to different users. We have evaluated the proposed method using real-world datasets. The results show that the method achieves higher accuracy compared to traditional ways of formulating the problem, while exhibiting additional desired properties of online, personalized, and adaptive learning. In eAssistant, we introduce a multi-dimensional learning model of conversations auto-triage, defined based on a user study and NLP-based information extraction techniques, to auto-triage user conversations on social collaboration and productivity tools.

DOI10.1145/3041021.3054147