In recent years, machine learning approaches, and in particular deep neural networks, have yielded significant improvements on several natural language processing and computer vision tasks; however, such breakthroughs have not yet been observed in the area of information retrieval. Besides the complexity of IR tasks, such as understanding the user's information needs, a main reason is the lack of high-quality and/or large-scale training data for many IR tasks. This necessitates studying how to design and train machine learning algorithms where there is no large-scale or high-quality data in hand. Therefore, considering the quick progress in development of machine learning models, this is an ideal time for a workshop that especially focuses on learning in such an important and challenging setting for IR tasks.
The goal of this workshop is to bring together researchers from industry, where data is plentiful but noisy, with researchers from academia, where data is sparse but clean, to discuss solutions to these related problems.
We invite two kinds of contributions: research papers (up to 6 pages) and position papers (up to 2 pages). Submissions must be in English, in PDF format, and should not exceed the appropriate page limit in the current ACM two-column conference format (including references and figures). Suitable LaTeX and Word templates are available from the ACM Website. The papers can represent reports of original research, preliminary research results, or proposals for new work. The review process is single-blind. Papers will be evaluated according to their significance, originality, technical content, style, clarity, relevance to the workshop, and likelihood of generating discussion. Authors should note that changes to the author list after the submission deadline are not allowed without permission from the PC Chairs. At least one author of each accepted paper is required to register for, attend, and present the work at the workshop. All short papers are to be submitted via EasyChair at https://easychair.org/conferences/?conf=lnd4ir.
Papers presented at the workshop 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.
University of Massachusetts Amherst
University of Amsterdam