Overview - LiveQA 2017

Proceedings | Data | Runs | Participants

The task addresses the automatic answering of consumer health questions received by the U.S. National Library of Medicine. We provided both training question-answer pairs, and test questions with reference answers. All questions were manually annotated with the main entities (foci) and question types. The medical task received eight runs from five participating teams. Different approaches have been applied, including classical answer retrieval based on question analysis and similar question retrieval. In particular, several deep learning approaches were tested, including attentional encoder-decoder networks, long short-term memory networks and convolutional neural networks. The training datasets were both from the open domain and the medical domain.

Track coordinator(s):

• Asma Ben Abacha, U.S. National Library of Medicine
• Eugene Agichtein, Emory University
• Yuval Pinter, Georgia Institute of Technology
• Dina Demner-Fushman, U.S. National Library of Medicine

Track Web Page: https://web.archive.org/web/20170729204820/https://sites.google.com/site/trecliveqa2017/

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