NAACL-HLT 2019: Call For System Demonstrations ==============================================
(Full information available at: http://naacl2019.org/calls/demos/ ) The system demonstration track at NAACL-HLT 2019 is a venue for papers describing system demonstrations, ranging from early prototypes to mature production-ready systems. Publicly available open-source or open-access systems are of special interest. All accepted demos are published in a companion volume of the conference proceedings. We expect at least one of the authors to present a live demo during a demo session at NAACL 2019, with an accompanying poster. This year, we will also present a Best Demo Paper Award. The winner will be chosen based on the contribution and completeness of the system, as assessed by reviewers and also based on the live demo at the conference. Submissions will undergo a *single-blind* reviewing process. So papers may include author and affiliation information, and freely make references to previously published material, and URLs. All submissions should be made electronically via START. Submissions must include: * A paper describing the motivation and technical details of the system, including visual aids (e.g., screenshots, snapshots, or diagrams). * A short (max. 3 minutes) video demonstrating the system. This video will be used to evaluate the paper, but won’t be published unless requested. Important Dates =============== Paper submissions due (incl. video) Monday, February 11, 2019 Notification of acceptance: Monday, March 18, 2019 Camera ready papers due: Monday, April 1, 2019 All deadlines are 11.59 pm UTC -12h. Contact Information =================== Email: [email protected] Demonstration track co-chairs: * Waleed Ammar, Allen Institute for Artificial Intelligence, USA * Annie Louis, University of Edinburgh, Scotland * Nasrin Mostafazadeh, Elemental Cognition, USA * General chair: Jill Burstein (Educational Testing Service) Please feel free to contact the demo chairs for any issues with your submissions or any questions regarding the relevance to the track.
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