Connectionists: BlackboxNLP 2019 - Call For Papers

[Dieuwke Hupkes]

BlackboxNLP 2019: Analyzing and interpreting neural networks for NLP -- ACL
2019 Workshop

ACL, Florence, Italy

Conference website: http://www.acl2019.org

Workshop website: https://blackboxnlp.github.io/

Submission deadline: April 19

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Neural networks have rapidly become a central component in NLP systems in
the last few years. The improvement in accuracy and performance brought by
the introduction of neural networks has typically come at the cost of our
understanding of the system: How do we assess what the representations and
computations are 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; Loula et al., 2018);

- Examining the performance of the network on simplified or formal
languages (e.g., Hupkes et al., 2018; Lake et al., 2018);

- Proposing modifications to neural network architectures that can make
them more interpretable (e.g., Palanki 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; Hupkes et al., 2017);

- Translating insights on neural networks interpretation from the vision
domain (e.g., Zeiler & Fergus, 2014) to language;

- Explaining model predictions (e.g., Lei et al., 2016; Alvarez-Melis &
Jaakkola, 2017): What are ways to explain specific decisions made by neural
networks?

- Adversarial examples in NLP (e.g., Ebrahimi et al., 2018; Belinkov &
Bisk, 2018): How to generate them and how to evaluate their quality?

- Open-source tools for analyzing neural networks in NLP (e.g., Strobelt et
al., 2018; Rikters, 2018).

- Evaluation of analysis results: How do we know that the analysis is
valid?

BlackboxNLP 2019 is the second BlackboxNLP workshop. The programme and
proceedings of the previous edition, which was held at EMNLP 2018, can be
found here: https://blackboxnlp.github.io/2018/.

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We call for two types of papers:

- 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.

Submission information:

- Submissions should follow the official ACL 2019 style guidelines.

- Submissions should be made through the Sonftconf START system:
https://www.softconf.com/acl2019/blackboxnlp.

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Important dates:

April 19 - Submission deadline

May 17 - Notification of acceptance

June 3 - Camera ready deadline

August 1 - Workshop

Note: All deadlines are 11:59PM UTC-12:00 ("anywhere on Earth").

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