Many prediction tasks in NLP involve assigning values to mutually
dependent variables. For example, when designing a model to
automatically perform linguistic analysis of a sentence or a document
(e.g., parsing, semantic role labeling, or discourse analysis), it is
crucial to model the correlations between labels. Many other NLP
tasks, such as machine translation, textual entailment, and
information extraction, can be also modeled as structured prediction
problems.
In order to tackle such problems, various structured prediction
approaches have been proposed, and their effectiveness has been
demonstrated. Studying structured prediction is interesting from both
NLP and machine learning (ML) perspectives. From the NLP perspective,
syntax and semantics of natural language are clearly structured and
advances in this area will enable researchers to understand the
linguistic structure of data. From the ML perspective, a large amount
of available text data and complex linguistic structures bring
challenges to the learning community. Designing expressive yet
tractable models and studying efficient learning and inference
algorithms become important issues.
Recently, there has been significant interest in non-standard
structured prediction approaches that take advantage of non-linearity,
latent components, and/or approximate inference in both the NLP and ML
communities. Researchers have also been discussing the intersection
between deep learning and structured prediction through the
DeepStructure reading group. This workshop intends to bring together
NLP and ML researchers working on diverse aspects of structured
prediction and expose the participants to recent progress in this
area. Topics of interest include, but are not limited to, the
following:
- Efficient learning and inference algorithms.
- Joint inference and learning approaches.
- Learning to search for NLP.
- Latent variable models.
- Integer linear programming and other modeling techniques.
- Structured training for non-linear models.
- Deep learning and neural network approaches for structured prediction.
- Structured prediction software.
- Structured prediction applications in NLP.
- Approximate inference for structured prediction.
* Submissions *
We invite the following two types of papers:
- Papers describing original, solid, and scientific research work
related to structured learning in NLP.
- Tutorial papers on structure prediction methods and/or applications.
All submissions must follow EMNLP 2016 formatting requirements, and
they must be in PDF. Papers should be less than 8 pages in length.
References do not count against this limit. The page limit serves as a
guideline and will not be strictly enforced. The official style files
are available at EMNLP16 Instructions for Submission
Reviewing will be double-blind, and thus no author information should
be included in the papers; self-reference should be avoided as well.
Submission is electronic and is managed by the START conference
management system at
https://www.softconf.com/emnlp2016/SPNLP/
Each submission will be reviewed by at least 2 program committee members.
* Important Dates *
- Aug 27: submission deadline ***DEADLINE EXTENDED***
- Sep 12: acceptance notification
- Sep 26: camera ready
- Nov 05: workshop at EMNLP in Austin, Texas, USA.
* Organizers *
- Kai-Wei Chang (University of Virginia)
- Ming-Wei Chang (Microsoft Research)
- Alexander Rush (Havard University)
- Vivek Srikumar (University of Utah)