Connectionists: 1st CFP: Deep Learning and Formal Languages: Building Bridges (workshop @ACL)

[Matthias Gallé]

Hohoho... you don't know if you want to ask Santa for a wooden train or a
quadcopter drone? Despair no more, you can have the best of both worlds:

Deep Learning and Formal Languages: Building Bridges

Deep Learning and Formal Languages: Building Bridges -- ACL 2019 Workshop

Florence, Italy

Website: https://sites.google.com/view/delfol-workshop-acl19

SUBMISSION DEADLINE: 26 April 2019


While deep learning and neural networks have revolutionized the field of
natural language processing, changed the habits of its practitioners and
opened up new research directions,  many aspects of the inner workings of
deep neural networks remain unknown.

At the same time, we have access to many decades of accumulated knowledge
on formal languages, grammar, and transductions, both weighted and
unweighted and for strings as well as trees: closure properties,
computational complexity of various operations, relationships between
various classes of them, and many empirical and theoretical results on
their learnability.

The goal of this workshop is to bring researchers together who are
interested in how our understanding of formal languages can contribute to
the understanding and design of neural network architectures for natural
language processing. For example, fundamental work on neural nets has
examined whether they could learn different classes of formal languages,
and reciprocally whether formal grammars or automata could closely
approximate neural networks. Recently we have seen new research directions
on what each formalism can bring to understand or improve the other. Topics
which fall within the purview of the workshop include, but are not limited
to


   -

   Learnability of formal languages with neural nets (both strong and weak
   learning)
   -

   Relationship between deep learning models and linguistically inspired
   formalisms
   -

   Connections between neural network architectures and classical
   computational models
   -

   Traditional formal grammars augmented through non-linearity
   -

   Hybrid models combining neural networks and finite state machines
   -

   The use of formal grammars to analyze and interpret the behavior of
   neural networks
   -

   Approximating neural networks with weighted automata and grammars
   -

   Including formal grammar constraints as symbolic priors in neural
   networks


We call for three types of papers:

(1) Regular workshop paper

(2) Extended abstracts

(3) Cross-submissions

Only (1) will be included in the workshop proceedings


Some recent work which falls within the scope of this call include:


   -

   Bridging CNNs, RNNs, and Weighted Finite-State Machines. Roy Schwartz,
   Sam Thomson,and Noah A Smith. (ACL 2018)
   -

   Rational Recurrences. Hao Peng, Roy Schwartz, Sam Thomson, Noah A.
   Smith. (ENMLP 2018)
   -

   Recurrent Neural Networks as Weighted Language Recognizers. Y. Chen, S.
   Gilroy, A. Maletti, J. May, and K. Knight.  (NAACL 2018)
   -

   Using Regular Languages to Explore the Representational Capacity of
   Recurrent Neural Architectures. Abhijit Mahalunkar and John D. Kelleher.
   (ICANN 2018)
   -

   Explaining black boxes on sequential data using weighted automata.
   Stéphane Ayache, Rémi Eyraud <http://www.lif.univ-mrs.fr/~reyraud> and
   Noé Goudian. (ICGI 2018)
   -

   Extracting Automata from Recurrent Neural Networks Using Queries and
   Counterexamples. Gail Weiss, Yoav Goldberg, and Eran Yahav. (ICML 2018)
   -

   Generalized Earley Parser: Bridging Symbolic Grammars and Sequence Data
   for Future Prediction. Siyuan Qi, Baoxiong Jia, and Song-Chun Zhu. (ICML
   2018)
   -

   Efficient Gradient Computation for Structured Output Learning with
   Rational and Tropical Losses. Corinna Cortes, Vitaly Kuznetsov, Mehryar
   Mohri, Dmitry Storcheus, Scott Yang (NIPS 2018)
   -

   Composing RNNs and FSTs for Small Data: Recovering Missing Characters in
   Old Hawaiian Text. Oiwi Parker Jones and Brendan Shillingford (IRASL
   workshop at NIPS 2018)
   -

   Verification of Recurrent Neural Networks Through Rule Extraction. Q
   Wang, K Zhang, X Liu, and CL Giles (arxiv.org 2018)
   -

   A Comparison of Rule Extraction for Different Recurrent Neural Network
   Models and Grammatical Complexity. Q Wang, K Zhang, II Ororbia, G
   Alexander, X Xing, X Liu, CL Giles (arxiv.org 2018)
   -

   Grammar Variational Autoencoder. Matt J. Kusner, Brooks Paige, José
   Miguel Hernández-Lobato. (ICML 2017)
   -

   Subregular Complexity and Deep Learning. Enes Avcu, Chihiro Shibata, and
   Jeffrey Heinz. (LAML 2017)
   -

   Recurrent Neural Network Grammars.  Chris Dyer, Adhiguna Kuncoro, Miguel
   Ballesteros, and Noah A. Smith. (NAACL 2016).
   -

   Weighting finite-state transductions with neural context. Pushpendre
   Rastogi, Ryan Cotterell, and Jason Eisner (NAACL 2016)

Programme Committee

   -

   Borja Balle, Amazon
   -

   Xavier Carreras, dMetrics
   -

   Shay B. Cohen, University of Edinburgh
   -

   Alex Clark, University of London
   -

   Ewan Dunbar, Université Paris Diderot
   -

   Marc Dymetman, Naver Labs Europe
   -

   Kyle Gorman, City University of New York
   -

   Hadrien Glaude, Amazon
   -

   John Hale, University of Georgia
   -

   Mans Hulden, University of Colorado
   -

   Franco Luque, University of Córdoba
   -

   Chihiro Shibata, Tokyo University of Technology
   -

   Adina Williams, FAIR



Organizers

   -

   Jason Eisner, Johns Hopkins University
   -

   Matthias Gallé, Naver Labs Europe
   -

   Jeffrey Heinz, Stony Brook University
   -

   Ariadna Quattoni, dMetrics
   -

   Guillaume Rabusseau, Université de Montréal / Mila
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