== LND4IR: The First International Workshop on Learning from Limited or Noisy Data for Information Retrieval Co-located with SIGIR 2018, Ann Arbor, Michigan, USA -- July 12, 2018 ==
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 <http://www.acm.org/publications/proceedings-template>.
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.
Relevant topics include, but are not limited to:
- Learning from noisy data for IR
- Learning from automatically constructed data
- Learning from implicit feedback data, e.g., click data
- Distant or weak supervision and learning from IR heuristics
- Unsupervised and semi-supervised learning for IR
- Transfer learning for IR
- Incorporating expert/domain knowledge to improve learning-based IR
- Learning from labeled features
- Incorporating IR axioms to improve machine learning models
Easychair Submission Link <https://easychair.org/conferences/?conf=lnd4ir>
== Important Dates:
- Submission deadline: May 4, 2018
- Paper notifications: May 25, 2018
- Camera-ready deadline: June 8, 2018
- Workshop Day: July 12, 2018
- Hamed Zamani, University of Massachusetts Amherst
- Mostafa Dehghani, University of Amsterdam
- Fernando Diaz, Spotify
- Hang Li, Toutiao
- Nick Craswell, Microsoft
Program Committee: TBD.
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