The second edition of the BlackboxNLP workshop will be collocated with ACL 2019 in Florence.
Archived information about the 2018 edition: blackboxnlp.github.io/2018.
We will have a limited number of travel awards for students whose papers were accepted for publication in the workshop, thanks to generous support from Microsoft, Google and Facebook. We will prioritize students with limited financial resources (e.g., who work in low-income countries or in graduate programs that are not well-funded). A link to the application website will be made available after acceptance notifications have been sent out. The deadline for applications is May 27.
Bio: Arianna Bisazza is an Assistant Professor in natural language processing at Leiden University, Netherlands. Her research aims at identifying intrinsic limitations of current language modeling paradigms as well as improving the quality of machine translation for challenging language pairs. She previously worked as a postdoc at the University of Amsterdam and as a research assistant at Fondazione Bruno Kessler. She obtained her PhD from the University of Trento, Italy, in 2013 and was awarded an NWO VENI grant in 2016.
Bio: Ari Morcos is a Research Scientist at Facebook AI Research working on understanding the mechanisms underlying neural network computation and function, and using these insights to build machine learning systems more intelligently. In particular, Ari has worked on understanding the properties predictive of generalization, methods to compare representations across networks, the role of single units in computation, and on strategies to measure abstraction in neural network representations. Previously, he worked at DeepMind in London, and earned his PhD in Neurobiology at Harvard University, using machine learning to study the cortical dynamics underlying evidence accumulation for decision-making.
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:
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.
We accept 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. An optional appendix may appear after the references in the same pdf file. 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. Archival papers will be included in the workshop proceedings and the ACL anthology.
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. Abstracts will be posted on the workshop website but will not be included in the proceedings.
Both papers and abstracts should follow the official ACL 2019 style guidelines and should be submitted via softconf:
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 other venues 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.
Papers posted to preprint servers such as arxiv can be submitted without any restrictions on when they were posted.
Tal Linzen (email@example.com) 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 first edition of BlackboxNLP, and has also organized two editions of the workshop on Cognitive Modeling and Computational Linguistics, co-located with EACL 2017 and with SCiL 2018.
Grzegorz Chrupała (firstname.lastname@example.org) is an Assistant Professor at the Department of Cognitive Science and Artificial Intelligence at Tilburg University. His research focuses 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 neural networks. His work has appeared in venues such as Computational Linguistics, ACL, EMNLP and CoNLL. He has served as area chair for ACL, EMNLP and CoNLL and he co-organized the first edition of BlackboxNLP.
Yonatan Belinkov (email@example.com) is a Postdoctoral Fellow at the Harvard School of Engineering and Applied Sciences (SEAS) and the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). His recent research focuses on representations of language in neural network models, with applications in machine translation and speech recognition. His research has been published at ACL, EMNLP, TACL, ICLR, and NeurIPS. His PhD dissertation at MIT analyzed internal language representations in deep learning models.
Dieuwke Hupkes (firstname.lastname@example.org) is a PhD student at the University of Amsterdam. The main focus of her research is understanding how recurrent neural networks can understand and learn the structures that occur in natural language. Developing methods to interpret and interact with neural networks has therefore been an important area of focus in her research. She authored 5 articles directly relevant to the workshop, one of them published in a top AI journal (Journal of Artificial Intelligence), and she is co-organizing a workshop on compositionality, neural networks, and the brain to be held at the Lorentz Center in the summer of 2019.
BlackboxNLP 2019 adheres to the ACL Anti-Harassment Policy.