29.2149, Calls: Computational Linguistics/Belgium

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LINGUIST List: Vol-29-2149. Thu May 17 2018. ISSN: 1069 - 4875.

Subject: 29.2149, Calls: Computational Linguistics/Belgium

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Date: Thu, 17 May 2018 13:23:53
From: Tal Linzen [tal.linzen at jhu.edu]
Subject: Workshop: Analyzing and Interpreting Neural Networks for NLP

 
Full Title: Workshop: Analyzing and Interpreting Neural Networks for NLP 
Short Title: BlackboxNLP 

Date: 31-Oct-2018 - 01-Nov-2018
Location: Brussels, Belgium 
Contact Person: Tal Linzen
Meeting Email: tal.linzen at jhu.edu
Web Site: http://blackboxnlp.github.io 

Linguistic Field(s): Computational Linguistics 

Call Deadline: 13-Jul-2018 

Meeting Description:

Neural networks have rapidly become a central component in language and speech
understanding systems in the last few years. The improvements in accuracy and
performance brought by the introduction of neural networks has typically come
at the cost of our understanding of the system: what are the representations
and computations that the network learns? The goal of this workshop is to
bring together people who are attempting to peek inside the neural network
black box, taking inspiration from machine learning, psychology, linguistics
and neuroscience. The topics of the workshop will include, but are not limited
to:

- Applying analysis techniques from neuroscience to analyze high-dimensional
vector representations (such as Haxby et al., 2001; Kriegeskorte, 2008) in
artificial neural networks;
- Analyzing the network’s response to strategically chosen inputs in order to
infer the linguistic generalizations that the network has acquired (e.g.,
Linzen et al., 2016);
- Examining the performance of the network on simplified or formal languages;
- Proposing modifications to neural network architectures that can make them
more interpretable (e.g., Palangi et al., 2017);
- Scaling up neural network analysis techniques developed in the connectionist
literature in the 1990s (Elman, 1991);
- Testing whether interpretable information can be decoded from intermediate
representations (e.g., Adi et al., 2017; Chrupała et al., 2017);
- Translating insights on neural network interpretation from the vision domain
(e.g., Zeiler & Fergus, 2014) to language.

Workshop organizers:

Tal Linzen, Johns Hopkins University (tal.linzen at jhu.edu)
Afra Alishahi, Tilburg University (a.alishahi at uvt.nl)
Grzegorz Chrupała, Tilburg University (g.a.chrupala at uvt.nl)


Call for Papers:

Important Dates:

July 13: Submission deadline.
August 3: Notification of acceptance.
October 31 or November 1: Workshop.

Submission Types:

- Archival papers. These are papers reporting on completed, original and
unpublished research, with maximum length of 8 pages + references. Papers
shorter than this maximum are also welcome. Accepted papers are expected to be
presented at the workshop and will be published in the workshop proceedings.
They should report on obtained results rather than intended work. These papers
will undergo double-blind peer-review, and should thus be anonymized.

- Extended abstracts. These may report on work in progress or may be cross
submissions that have already appeared in a non-NLP venue. The extended
abstracts are of maximum 2 pages + references. These submissions are
non-archival in order to allow submission to another venue. The selection will
not be based on a double-blind review and thus submissions of this type need
not be anonymized.

Both categories of submissions should use EMNLP 2018 templates:
- Latex: http://emnlp2018.org/downloads/emnlp18-latex.zip
- Word: http://emnlp2018.org/downloads/emnlp18-word.zip 

Papers and abstracts should be submitted via softconf:
https://www.softconf.com/emnlp2018/BlackboxNLP/




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