Connectionists: 2nd CfP: ACL workshop - Deep Learning and Formal Languages: Building Bridges

[Matthias Gallé]

Deep Learning and Formal Languages:
Building Bridges


Deep Learning and Formal Languages: Building Bridges -- ACL 2019 Workshop
August 2nd - 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


The programme will consist of invited speakers, presentations of selected
contributions and a poster-session for discussion. We are delighted to
announce the following list of speakers:


   - Rémi Eyraud (Aix-Marseilles University)
   - Robert Frank (Yale University)
   - John Kelleher (Dublin Institute of Technology)
   - Kevin Knight (Didi)
   - Ariadna Quattoni (dMetrics)
   - Noah Smith (University of Washington)


We *call *for three types of papers:

(1) Regular workshop paper: original and unpublished work in the context of
this workshop, up to 8 pages (excluding references). Will be included in
the workshop proceedings.

(2) Extended abstracts: on-going work, intermediate results or positions
papers. Up to 4 pages (excluding references), which will not be included in
the proceedings.

(3) Cross-submissions: relevant work published elsewhere in the last 12
months. Together with the pdf, you will be asked at submission time the
reference of the original paper. Will not be included in the workshop
proceedings.


Note that we will be able to offer *travel grants *to some students, thanks
to generous support from Naver Labs.



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 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)

Connecting Weighted Automata and Recurrent Neural Networks Through Spectral
Learning. G. Rabusseau, T. Li and D. Precup. (AISTATS 2019)


*Programme Committee*

   - Raphaël Bailly, Université Paris 1
   - 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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