SIGIR 2017 Workshop on Neural Information Retrieval (Neu-IR'17)

Abstract

In recent years, deep neural networks have yielded significant performance improvements in application areas such as speech recognition, computer vision, and machine translation. This has led to expectations in the information retrieval (IR) community that these novel machine learning approaches are likely to demonstrate a similar scale of breakthroughs on IR tasks within the next couple of years. In the Neu-IR (pronounced "new IR") 2016 workshop, however, there was a growing concern that the lack of availability of large scale training and evaluation datasets may be hindering the research community from making adequate progress in this area. It was also highlighted that the community would benefit from establishing a shared public repository of neural IR models and shared evaluation resources for better reproducibility and speed of experimentation. After the first successful Neu-IR workshop at SIGIR 2016, our goal this year will be to host a highly interactive full-day workshop to bring the neural IR community together to specifically address these key challenges facing this line of research. The workshop will request the community to submit proposals on generating large scale benchmark collections, building a shared model repository, and standardizing frameworks appropriate for evaluating deep neural network models. In addition, the workshop will provide a forum for the growing community of IR researchers to present their recent (published and unpublished) work involving (shallow or deep) neural network based approaches in an interactive poster session.