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CVSC 2015 : The 3rd Workshop on Continuous Vector Space Models and their Compositionality


When Jul 31, 2015 - Jul 31, 2015
Where Peking
Submission Deadline May 14, 2015
Notification Due Jun 4, 2015
Final Version Due Jun 21, 2015

Call For Papers

First Call for Papers

In recent years, there has been a growing interest in algorithms that learn and use continuous representations for words, phrases, or documents in many natural language processing applications. Among many others, influential proposals that illustrate this trend include latent Dirichlet allocation, neural network based language models and spectral methods. These approaches are motivated by improving the generalization power of the discrete standard models, by dealing with the data sparsity issue and by efficiently handling a wide context. Despite the success of single word vector space models, they are limited since they do not capture compositionality. This prevents them from gaining a deeper understanding of the semantics of longer phrases, sentences and documents.

Regarding this issue, some pertinent questions arise: should word/phrase/sentence representations be of the same sort? Could different linguistic levels require different modelling approaches ? Is compositionality determined by syntax, and if so, how do we learn/define it? Should word representations be fixed and obtained distributionally, or should the encoding be variable? Should word representations be task-specific, or should they be general?

In this workshop, we invite submissions of papers on continuous vector space models for natural language processing. Topics of interest include, but are not limited to:
* Neural networks
* Spectral methods
* Distributional semantic models
* Language modeling for automatic speech recognition, statistical machine translation, and information retrieval
* Automatic annotation of texts
* Phrase and sentence-level distributional representations
* The role of syntax in compositional models
* Formal and distributional semantic models
* Language modeling for logical and natural reasoning
* Integration of distributional representations with other models
* Multi-modal learning for distributional representations
* Knowledge base embedding


The workshop will showcase presentations from 4 to 6 keynote speakers. The confirmed speakers are:
• Yoav Goldberg (Bar Ilan University)
• Jason Weston (Facebook AI Research)
• Kyunghyun Cho (Université de Montréal)


Authors should submit a full paper of up to 8 pages in electronic, PDF format, with up to 2 additional pages for references. The reported research should be substantially original. The papers will be presented orally or as posters.

All submissions must be in PDF format and must follow the ACL 2015 formatting requirements (see the ACL 2015 Call For Papers Reviewing will be double-blind, and thus no author information should be included in the papers; self-reference should be avoided as well. Submissions must be made through the Softconf website set up for this workshop:

Accepted papers will appear in the workshop proceedings, where no distinction will be made between papers presented orally or as posters.


14 May 2015 : Submission deadline
4 June 2015 : Notification of acceptance
21 June 2015 : Camera-ready deadline
31 July 2015 : Workshop


Alexandre Allauzen (LIMSI-CNRS/Université Paris-Sud, France)
Edward Grefenstette (University of Oxford, UK)
Karl Moritz Hermann (University of Oxford, UK)
Hugo Larochelle (Université de de Sherbrooke, Canada)
Scott Wen-tau Yih (Microsoft Research, USA)


Marco Baroni, University of Trento
Yoshua Bengio, Université de Montreal
Phil Blunsom, University of Oxford
Antoine Bordes, Facebook
Leon Bottou, Microsoft
Stephen Clark, University of Cambridge
Shay Cohen, University of Edinburgh
Georgiana Dinu, University of Trento
Kevin Duh, Nara Institute of Science and Technology
Yoav Goldberg, Bar Ilan University
Andriy Mnih, University College London
Mehrnoosh Sadrzadeh, University of London
Mark Steedman, University of Edinburgh
Peter Turney, NRC
Jason Weston, Facebook
Guillaume Wisniewski, LIMSI-CNRS

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