91 citations · 275 across the 12 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…
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…