2015 Workshop on Cognitive Modeling and Computational Linguistics (CMCL)
(CMCL 2015)

CALL FOR PAPERS

Cognitive Modeling and Computational Linguistics 2015 (CMCL-2015)
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This workshop provides a venue for work in computational
psycholinguistics: the computational and mathematical modeling of
linguistic generalization, development, and processing. We invite
contributions that apply methods from computational linguistics to
problems in the cognitive modeling of any and all natural
language-related abilities.

The 2015 workshop will be co-located with NAACL-HLT and follows in the
tradition of earlier CMCL meetings at ACL 2010, ACL 2011,
NAACL-HLT 2012, ACL 2013, ACL 2014.

Scope and Topics
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The workshop invites a broad spectrum of work in the cognitive science
of language, at all levels of analysis from sounds to discourse and on
both learning and processing. Topics include, but are not limited to:

* incremental parsers for diverse grammar formalisms
* derivations of quantitative measures of comprehension difficulty, or
predictions regarding generalization in language learning
* stochastic models of factors encouraging one production or interpretation
over its competitors
* models of semantic/pragmatic interpretation, including psychologically
realistic notions of word meaning, phrase meaning, composition, and
pragmatic inference
* models and empirical analysis of the relationship between mechanistic
psycholinguistic principles and pragmatic or semantic adaptation
* models of human language acquisition and/or adaptation in a changing
linguistic environment
* models of linguistic information propagation and language change in
communication networks
* models of lexical acquisition, including phonology, morphology, and
semantics
* psychologically motivated models of grammar induction or semantic learning

Submissions are especially welcomed that combine computational
modeling work with experimental or corpus data to test theoretical
questions about the nature of human language acquisition,
comprehension, and/or production.

Submissions
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This call solicits full papers reporting original and unpublished
research that combines cognitive modeling and computational
linguistics. Accepted papers are expected to be presented at the
workshop and will be published in the workshop proceedings. They
should emphasize obtained results rather than intended work, and
should indicate clearly the state of completion of the reported
results. A paper accepted for presentation at the workshop must not be
presented or have been presented at any other meeting with publicly
available proceedings. No submission should be longer than necessary, up
to a maximum 8 pages plus two additional pages containing references.

If essentially identical papers are submitted to other conferences or
workshops as well, this fact must be indicated at submission time.

To facilitate double-blind reviewing, submitted manuscripts should not
include any identifying information about the authors.

Submissions must be formatted using ACL 2015 submission guidelines at

http://naacl.org/naacl-hlt-2015/call-for-papers.html

Submission style templates are available at:

http://naacl.org/naacl-pubs/

Contributions should be submitted in PDF via the submission site:

https://www.softconf.com/naacl2015/cmcl

The submission deadline is 11:59PM Pacific Time on March 6, 2015.

Best Student Paper
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The best paper whose first author is a student will receive the Best
Student Paper award. All accepted CMCL papers will be published
in the workshop proceedings as is customary at ACL conferences.

Important Dates
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Submission deadline: 6 March 2015
Notification of acceptance: 24 March 2015
Camera-ready versions due: 3 April 2015
Workshop: June 4, 2015

Workshop Chairs
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Tim O’Donnell
Department of Brain and Cognitive Sciences, Massachusetts Institute of
Technology, USA

Marten van Schijndel
Department of Linguistics, The Ohio State University, USA

Program Committee
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Omri Abend, University of Edinburgh
Steven Abney, University of Michigan
Afra Alishahi, Tilburg University
Libby Barak, University of Toronto
Marco Baroni, University of Trento
Robert Berwick, MIT
Klinton Bicknell, Northwestern University
Christos Christodoulopoulos, University of Illinois at Urbana-Champaign
Alexander Clark, King’s College
Moreno Cocco, University of Lisbon
Jennifer Culbertson, George Mason University
Vera Demberg, Saarland University
Brian Dillon, University of Massachusetts Amherst
Micha Elsner, The Ohio State University
Naomi Feldman, University of Maryland
Alex Fine, University of Illinois at Urbana-Champaign
Bob Frank, Yale University
Michael Frank, Stanford University
Stefan Frank, Radboud University Nijmegen
Stella Frank, Edinburgh University
Ted Gibson, MIT
Sharon Goldwater, Edinburgh University
Carlos Gomez Gallo, Northwestern University
Noah Goodman, Stanford University
Thomas Graf, Stony Brook University
John Hale, Cornell University
Jeffrey Heinz, University of Delaware
Tim Hunter, University of Minnesota
Mark Johnson, Macquarie University
Frank Keller, University of Edinburgh
Shalom Lappin, King’s College
Roger Levy, UCSD
Pavel Logacev, Potsdam University
Titus von der Malsburg, UCSD
Rebecca Morley, The Ohio State University
Aida Nematzadeh, University of Toronto
Ulrike Pado, Hochschule fuer Technik, Stuttgart
Bozena Pajak, Northwestern University
Lisa Pearl, UC Irvine
Massimo Poesio, University of Essex
Ting Qian, Brown University
Roi Reichart, Technion University
David Reitter, Penn State University
William Schuler, The Ohio State University
Nathaniel Smith, University of Edinburgh
Ed Stabler, UCLA
Mark Steedman, University of Edinburgh
Patrick Sturt, University of Edinburgh
Colin Wilson, Johns Hopkins University
Alessandra Zarcone, Saarland University of Massachusetts
Jelle Zuidema, University of Amsterdam

Read more:
http://portal.aclweb.org/content/2015-workshop-cognitive-modeling-and-computational-linguistics-cmcl

The ESSLLI program is online – and it includes a course on Vectorial and Neural Models of Compositional Semantics!

http://esslli2015.org/?q=courses 

Our paper “Inside-Outside Semantics: A Framework for Neural Models of Semantic Composition” was accepted for the NIPS 2014 Workshop on Deep Learning and Representation Learning.

Here is the abstract:

Inside-Outside Semantics: A Framework for Neural Models of Semantic Composition

Phong Le & Willem Zuidema

The Recursive Neural Network (RNN) model and its extensions have been shown to be powerful tools for semantic composition with successes in many natural language processing (NLP) tasks. However, in this paper, we argue that the RNN model is restricted to a subset of NLP tasks where semantic compositionality plays a role. We propose an extension called Inside-Outside Semantics. In our framework every node in a parse tree is associated with a pair of representations, the inner representation for representing the content under the node, and the outer representation for representing its surrounding context. We demonstrate how this allows us to develop neural models for a much broader class of NLP tasks and for supervised as well as unsupervised learning. Our neural-net model, Inside-Outside Recursive Neural Network, performs on par with or better than the state-of-the-art (neural) models in word prediction, phrase-similarity judgments and semantic role labelling.

Phong presented our paper “The Inside-Outside Recursive Neural Network model for Dependency Parsing” at EMNLP in Qatar. Here is the abstract:

The Inside-Outside Recursive Neural Network model for Dependency Parsing

(published pdf here)

Phong Le & Willem Zuidema

We propose the first implementation of an infinite-order generative dependency
model. The model is based on a new recursive neural network architecture, the
Inside-Outside Recursive Neural Network. This architecture allows information to
flow not only bottom-up, as in traditional recursive neural networks, but also topdown.
This is achieved by computing content as well as context representations for any constituent, and letting these representations interact. Experimental results on the English section of the Universal Dependency Treebank show that the infinite-order model achieves a perplexity
seven times lower than the traditional third-order model using counting, and tends to choose more accurate parses in k-best lists. In addition, reranking with this model achieves state-of-the-art unlabelled attachment scores and unlabelled exact match scores.

http://www.talkingbrains.org/2014/10/dear-crowd-please-crowd-solve-question.html

Members of the CLC Lab are:

Jelle Zuidema (UD)

Remko Scha (professor emeritus)

Daniel Wiechmann (UD)

Phong Le (PhD student)

Raquel Alhama (PhD student, FNWI)

Dieuwke Hupkes (research assistant)

Gideon Borensztajn (postdoc)

Jonathan Mallison (MSc student)

Stijn de Gooijer (MSc student)