|Research on human-computer interaction plays a central role across multiple teams at Microsoft Research. Our work is focused on advancing the way users interact with computing devices. This includes search, access, and information management, the display of complex data and information, user modeling and activity recognition, efficient input and interaction, the role of automation, and the coupling of intelligent systems with direct manipulation.|
- ASHA AssistThis project focuses on rural government maternal health workers in India (called Accredited Social Health Activists, or ASHAs), using a tool called ASHA Assist to help ASHAs engage their clients in persuasive discussions about various topics related to maternal health. ASHA Assist consists of interactive videos on mobile phones, covering topics related to maternal health for use in counseling their clients.
- Spoken Language UnderstandingSpoken language understanding (SLU) is an emerging field in between the areas of speech processing and natural language processing. The term spoken language understanding has largely been coined for targeted understanding of human speech directed at machines. This project covers our research on SLU tasks such as domain detection, intent determination, and slot filling, using data-driven methods.
- Reducing Disruption from Subtle Information Delivery during a ConversationWith proliferation of ubiquitous access to information, the question arises of how distracting processing information can be in social settings, especially during a face-to-face conversation. In this paper, we investigate how much information users can consume during a conversation and what information delivery mode, via audio or visual aids, helps them effectively conceal the fact that they are receiving information.
- [Big] Data StudiesData is all the buzz. It's being seen in everything and found everywhere. But what are the consequences of this vision of a data-rich world for those of us on the street; what impact if any does it have on our everyday experiences and with the things that matter most to us. Here, we aim to reflect on the rise of (big) data and investigate what it does mean for us, and what it could come to mean.
Anna Macaranas, Gina Venolia, Kori Inkpen, and John Tang, Sharing Experiences over Video: Watching Video Programs Together at a Distance, in Proc. INTERACT 2013, Springer, September 2013
Larry Heck, Dilek Hakkani-Tur, and Gokhan Tur, Leveraging Knowledge Graphs for Web-Scale Unsupervised Semantic Parsing, in Proceedings of Interspeech, International Speech Communication Association, August 2013
Asli Celikyilmaz, Gokhan Tur, and Dilek Hakkani-Tur, IsNL? A Discriminative Approach to Detect Natural Language Like Queries for Conversational Understanding, Annual Conference of the International Speech Communication Association (Interspeech), August 2013
Sanjay Kairam, Meredith Ringel Morris, Jaime Teevan, Daniel Liebling, and Susan Dumais, Towards Supporting Search over Trending Events with Social Media, in Proceedings of ICWSM 2013, AAAI, July 2013
Munmun De Choudhury, Scott Counts, Eric Horvitz, and Michael Gamon, Predicting Depression via Social Media., AAAI, July 2013