Call For Papers
A key ambition of AI is to render computers able to evolve and interact with the real world. This can be made possible only if the machine is able to produce an interpretation of its available modalities (image, audio, text, etc.) which can be used to support reasoning and taking appropriate actions. Computational linguists use the term “semantics” to refer to the possible interpretations of natural language expressions and there is recent work in “learning semantics” – finding (in an automated way) these interpretations. However, “semantics” are not restricted to the natural language (and speech) modality, and are also pertinent to visual modalities. Hence, knowing visual concepts and common relationships between them would certainly provide a leap forward in scene analysis and in image parsing akin to the improvement that language phrase interpretations would bring to data mining, information extraction or automatic translation, to name a few.
Progress in learning semantics has been slow mainly because this involves sophisticated models which are hard to train, especially since they seem to require large quantities of precisely annotated training data. However, recent advances in learning with weak, limited and indirect supervision led to the emergence of a new body of research in semantics based on multi-task/transfer learning, on learning with semi/ambiguous/indirect supervision or even with no supervision at all. Hence, this special issue invites paper submissions on recent work for learning semantics of natural language, vision, speech, etc.
Papers should address at least some of the following questions:
- How should meaning representations be structured to be easily interpretable by a computer and still express rich and complex knowledge?
- What is a realistic supervision setting for learning semantics?
- How can we learn sophisticated representations with limited supervision?
- How can we jointly infer semantics from several modalities?
Submission deadline: May 1, 2012 (passed)
First review results:
July 30, 2012. August 7, 2012.
Final drafts: September 30, 2012
Papers must be submitted online, selecting the article type that indicates this special issue. Peer reviews will follow the standard Machine Learning journal review process. It is the policy of the Machine Learning journal that no submission, or substantially overlapping submission, be published or be under review at another journal or conference at any time during the review process. Papers extending previously published conference papers are acceptable, as long as the journal submission provides a significant contribution beyond the conference paper, and the overlap is described clearly at the beginning of the journal submission. Complete manuscripts of full length are expected, following the MLJ guidelines in http://www.springer.com/computer/ai/journal/10994 .
Antoine Bordes (firstname.lastname@example.org)
Léon Bottou (email@example.com)
Ronan Collobert (firstname.lastname@example.org)
Dan Roth (email@example.com)
Jason Weston (firstname.lastname@example.org)
Luke Zettlemoyer (email@example.com)