<< Final Workshop Report >>
Submission Deadline: May 16
Acceptance Notifications: June 6
Camera-ready Deadline: June 17
Workshop: July 21
The first international Neu-IR (pronounced “new IR”) workshop on neural information retrieval will be hosted at SIGIR 2016 in Pisa, Tuscany, Italy on 21 July, 2016.
(The final report on the workshop is available here.)
In recent years, deep neural networks have yielded significant performance improvements on speech recognition and computer vision tasks, as well as led to exciting breakthroughs in novel application areas such as automatic voice translation, image captioning, and conversational agents. Despite demonstrating good performance on natural language processing (NLP) tasks, the performance of deep neural networks on IR tasks has had relatively less scrutiny.
The lack of many positive results in the area of information retrieval is partially due to the fact that IR tasks such as ranking are fundamentally different from NLP tasks, but also because the IR and neural network communities are only beginning to focus on the application of these techniques to core information retrieval problems. Given that deep learning has made such a big impact, first on speech processing and computer vision and now, increasingly, also on computational linguistics, it seems clear that deep learning will have a major impact on information retrieval and that this is an ideal time for a workshop in this area. Our focus is on the applicability of deep neural networks to information retrieval: demonstrating performance improvements on public or private information retrieval datasets, identifying key modelling challenges and best practices, and thinking about what insights deep neural network architectures give us about information retrieval problems.
Neu-IR 2016 will be a highly interactive full day workshop that will provide a forum for academic and industrial researchers working at the intersection of IR and neural networks. The purpose is to provide an opportunity for people to present new work and early results, compare notes on neural network toolkits, share best practices, and discuss the main challenges facing this line of research.
Please use the tabs above to navigate to see the program, the accepted papers and other details of this workshop.