Neu-IR 2017 : The SIGIR Workshop on Neural Information Retrieval
Call For Papers
We solicit submission of papers of two to six pages (excluding references), representing reports of original research, preliminary research results, proposals for new work, descriptions of neural network based toolkits tailored for IR, and position papers. Papers presented at the Neu-IR 2017 will be required to be uploaded to arXiv.org but will be considered non-archival, and may be submitted elsewhere (modified or not), although the workshop site will maintain a link to the arXiv versions. This makes the workshop a forum for the presentation and discussion of current work, without preventing the work from being published elsewhere.
For Neu-IR 2017 we are especially interested in receiving from the community proposals on generating large scale benchmark collections, building a shared model repository, and standardizing frameworks appropriate for evaluating deep neural network models.
In addition, Neu-IR 2017 welcomes submissions relevant to the following main themes:
The application of neural network models in IR tasks, including but not limited to:
Full text document retrieval, passage retrieval, question answering
Web search, searching social media, distributed information retrieval, entity ranking
Learning to rank combined with neural network based representation learning
User and task modelling, personalized search, diversity
Query formulation assistance, query recommendation, conversational search
Fundamental modelling challenges faced in such applications, including but not limited to:
Learning dense representations for long documents
Dealing with rare queries and rare words
Modelling text at different granularities (character, word, passage, document)
Compositionality of vector representations
Jointly modelling queries, documents, entities and other structured/knowledge data
Best practices for research and development in the area, dealing with concerns such as:
Finding sufficient publicly-available training data
Baselines, test data, avoiding overfitting
Neural network toolkits
Real-world use cases, deployment at scale
All papers will be peer reviewed (single-blind) by the program committee and judged by their relevance to the workshop, especially to the main themes identified above, and their potential to generate discussion. All submissions must be formatted according to the ACM SIG proceedings template. Please note that at least one of the authors of each accepted paper must register for the workshop and present the paper in-person.