The workshop will be collocated with EMNLP 2018 in Brussels. The workshops will be held on October 31 and November 1.
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:
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 These are papers reporting on completed, original and unpublished research, with maximum length of 8 pages + references.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:
Both papers and abstracts should be submitted via softconf: https://www.softconf.com/emnlp2018/BlackboxNLP/
Accepted submissions will be presented at the workshop: most as posters, some as oral presentations (determined by the program committee).
Dual submissions of archival papers with EMNLP (or another conference) are allowed. Please let us know as soon as possible if you decide to withdraw a paper accepted elsewhere. Also please consider that dual submissions increase reviewing burden for the whole community.
Leila Wehbe is a postdoctoral researcher at the Gallant Lab in the Helen Wills Neuroscience Institute at UC Berkeley. Previously, she obtained her PhD from the Machine Learning Department and the Center for the Neural Basis of Cognition at Carnegie Mellon University, where she worked with Tom Mitchell. She works on studying language representations in the brain when subjects engage in naturalistic language tasks. She combines functional neuroimaging with natural language processing and machine learning tools to build spatiotemporal maps of the information represented in the brain during language processing.
Graham Neubig is an assistant professor at the Language Technologies Intitute of Carnegie Mellon University. His work focuses on natural language processing, specifically multi-lingual models that work in many different languages, and natural language interfaces that allow humans to communicate with computers in their own language. Much of this work relies on machine learning to create these systems from data, and he is also active in developing methods and algorithms for machine learning over natural language data. He publishes regularly in the top venues in natural language processing, machine learning, and speech, and his work occasionally wins awards such as best papers at EMNLP, EACL, and WNMT. He is also active in developing open-source software, and is the main developer of the DyNet neural network toolkit.
Yoav Goldberg is a Senior Lecturer at Bar Ilan University’s Computer Science Department. Before that, he was a Research Scientist at Google Research New York. He works on problems related to Natural Language Processing and Machine Learning. In particular he is interested in syntactic parsing, structured-prediction models, learning for greedy decoding algorithms, multilingual language understanding, and cross domain learning. Lately, he is also interested in neural network based methods for NLP. He recently published a book on the subject.
Tal Linzen is an Assistant Professor of Cognitive Science at Johns Hopkins University. He develops computational cognitive models of language. In addition to his work in psycholinguistics and cognitive neuroscience, he has studied the syntactic capabilities of contemporary artificial neural networks and the linguistic information encoded in word embeddings, in work that has appeared in TACL, EACL and CoNLL. He has co-organized the workshop on Cognitive Modeling and Computational Linguistics which was co-located with EACL 2017, and is co-organizing the next installation of the same workshop at the Society for Computation in Linguistics in January 2018.
Afra Alishahi is an Associate Professor of Cognitive Science and Artificial Intelligence at Tilburg University, the Netherlands. Her main research interest is developing computational models for studying the process of human language acquisition. Recently she has been studying the emergence of linguistic structure in grounded models of language learning. She has chaired CoNLL 2015, and organized the EACL Workshop on Cognitive Aspects of Computational Language Acquisition in 2009.
Grzegorz Chrupała is an Assistant Professor at the Department of Cognitive Science and Artificial Intelligence at Tilburg University. His recent research focus has been on computational models of language learning from multimodal signals such as speech and vision and on the analysis and interpretability of representations emerging in multilayer recurrent neural networks. He regularly serves on program committees of major NLP and AI conferences, workshops and journals. He was area co-chair for Machine Learning at ACL 2017, for Discourse and Dialogue, Summarization and Generation, and Multimodal NLP and Speech at EMNLP 2018, and general chair for Benelearn 2018.