Call for papers ===============
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.
Important dates ===============
July 19: Submission deadline (extended). 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 These are papers reporting on completed, original and unpublished research, with maximum length of 8 pages + references.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/
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) -------------- next part -------------- A non-text attachment was scrubbed... Name: not available Type: text/html Size: 4137 bytes Desc: not available URL: <https://mailman.uib.no/public/corpora/attachments/20180709/01ab130a/attachment.txt>