91 citations · 275 across the 12 of their papers we have counts for
14 papers
One Agent To Rule Them All: Towards Multi-agent Conversational AI
Christopher Clarke, Joseph Joshua Peper, Karthik Krishnamurthy +6
The increasing volume of commercially available conversational agents (CAs) on the market has resulted in users being burdened with learning and adopting multiple agents to accompl…
Towards Supporting Programming Education at Scale via Live Streaming
Yan Chen, Walter S. Lasecki, Tao Dong
Live streaming, which allows streamers to broadcast their work to live viewers, is an emerging practice for teaching and learning computer programming. Participation in live stream…
Sifter: A Hybrid Workflow for Theme-based Video Curation at Scale
Yan Chen, Andrés Monroy-Hernández, Ian Wehrman +3
User-generated content platforms curate their vast repositories into thematic compilations that facilitate the discovery of high-quality material. Platforms that seek tight editori…
The Eighth Dialog System Technology Challenge
Seokhwan Kim, Michel Galley, Chulaka Gunasekara +18
This paper introduces the Eighth Dialog System Technology Challenge. In line with recent challenges, the eighth edition focuses on applying end-to-end dialog technologies in a prag…
CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases
Tao Yu, Rui Zhang, He Yang Er +21
We present CoSQL, a corpus for building cross-domain, general-purpose database (DB) querying dialogue systems. It consists of 30k+ turns plus 10k+ annotated SQL queries, obtained f…
HEIDL: Learning Linguistic Expressions with Deep Learning and Human-in-the-Loop
Yiwei Yang, Eser Kandogan, Yunyao Li +2
While the role of humans is increasingly recognized in machine learning community, representation of and interaction with models in current human-in-the-loop machine learning (HITL…
A Case for Backward Compatibility for Human-AI Teams
Gagan Bansal, Besmira Nushi, Ece Kamar +3
AI systems are being deployed to support human decision making in high-stakes domains. In many cases, the human and AI form a team, in which the human makes decisions after reviewi…
Dialog System Technology Challenge 7
Koichiro Yoshino, Chiori Hori, Julien Perez +14
This paper introduces the Seventh Dialog System Technology Challenges (DSTC), which use shared datasets to explore the problem of building dialog systems. Recently, end-to-end dial…
A Large-Scale Corpus for Conversation Disentanglement
Jonathan K. Kummerfeld, Sai R. Gouravajhala, Joseph Peper +6
Disentangling conversations mixed together in a single stream of messages is a difficult task, made harder by the lack of large manually annotated datasets. We created a new datase…
"Is there anything else I can help you with?": Challenges in Deploying an On-Demand Crowd-Powered Conversational Agent
Ting-Hao Kenneth Huang, Walter S. Lasecki, Amos Azaria +1
Intelligent conversational assistants, such as Apple's Siri, Microsoft's Cortana, and Amazon's Echo, have quickly become a part of our digital life. However, these assistants have…
Understanding Task Design Trade-offs in Crowdsourced Paraphrase Collection
Youxuan Jiang, Jonathan K. Kummerfeld, Walter S. Lasecki
Linguistically diverse datasets are critical for training and evaluating robust machine learning systems, but data collection is a costly process that often requires experts. Crowd…
Creating Interactive Behaviors in Early Sketch by Recording and Remixing Crowd Demonstrations
Sang Won Lee, Yi Wei Yang, Shiyan Yan +6
In the early stages of designing graphical user interfaces (GUIs), the look (appearance) can be easily presented by sketching, but the feel (interactive behaviors) cannot, and ofte…
Tuning the Diversity of Open-Ended Responses from the Crowd
Walter S. Lasecki, Christopher M. Homan, Jeffrey P. Bigham
Crowdsourcing can solve problems that current fully automated systems cannot. Its effectiveness depends on the reliability, accuracy, and speed of the crowd workers that drive it.…
Crowd Memory: Learning in the Collective
Walter S. Lasecki, Samuel C. White, Kyle I. Murray +1
Crowd algorithms often assume workers are inexperienced and thus fail to adapt as workers in the crowd learn a task. These assumptions fundamentally limit the types of tasks that s…