Shalom Lappin is a Professor of Computational Linguistics at the University of Gothenburg, Professor of Natural Language Processing at Queen Mary University of London and Emeritus Professor of Computational Linguistics at King’s College London. His research focuses on the application of machine learning and probabilistic models to the representation and the acquisition of linguistic knowledge.Jean-Philippe Bernardy is a researcher at the University of Gothenburg. His main research interest is in interpretable linguistic models, in particular, those built from first principles of algebra, probability and geometry.

“Lappin and Bernardy have assembled a great set of researchers who work on linguistic, cognitive science and natural language processing in deep neural network approaches to language. The result is a state of the art collection of interest to anyone with interests in DNNs and their connection to human language.” --Edward A. F. Gibson, Professor, MIT Department of Brain & Cognitive Sciences

• 1. On the Proper Role of Linguistically Oriented Deep Net Analysis in Linguistic Theorizing by Marco Baroni. 2. What Artificial Neural Networks Can Tell Us About Human Language Acquisition by Alex Warstadt and Samuel R. Bowman. 3. Grammar through Spontaneous Order by Nick Chater and Morten H. Christiansen. 4. Language is Acquired in Interaction by Eve V. Clark. 5. Why Algebraic Systems aren’t Sufficient for Syntax by Ben Ambridge. 6. Learning Syntactic Structures from String Input by Ethan Gotlieb Wilcox, Jon Gauthier, Jennifer Hu, Peng Qian, and Roger Levy. 7. Analyzing Discourse Knowledge in Pre-Trained LMs by Sharid Lo´aiciga. 8. Linguistically Guided Multilingual NLP by Olga Majewska, Ivan Vuli´c, and Anna Korhonen. 9. Word Embeddings are Word Story Embeddings (and that’s fine) by Katrin Erk and Gabriella Chronis. 10. Algebra and Language: Reasons for (Dis)content by Lawrence S. Moss. 11. Unitary Recurrent Networks by Jean-Philippe Bernardy and Shalom Lappin.

Shalom Lappin is a Professor of Computational Linguistics at the University of Gothenburg, Professor of Natural Language Processing at Queen Mary University of London and Emeritus Professor of Computational Linguistics at King’s College London. His research focuses on the application of machine learning and probabilistic models to the representation and the acquisition of linguistic knowledge.Jean-Philippe Bernardy is a researcher at the University of Gothenburg. His main research interest is in interpretable linguistic models, in particular, those built from first principles of algebra, probability and geometry.

“Lappin and Bernardy have assembled a great set of researchers who work on linguistic, cognitive science and natural language processing in deep neural network approaches to language. The result is a state of the art collection of interest to anyone with interests in DNNs and their connection to human language.” --Edward A. F. Gibson, Professor, MIT Department of Brain & Cognitive Sciences

• 1. On the Proper Role of Linguistically Oriented Deep Net Analysis in Linguistic Theorizing by Marco Baroni. 2. What Artificial Neural Networks Can Tell Us About Human Language Acquisition by Alex Warstadt and Samuel R. Bowman. 3. Grammar through Spontaneous Order by Nick Chater and Morten H. Christiansen. 4. Language is Acquired in Interaction by Eve V. Clark. 5. Why Algebraic Systems aren’t Sufficient for Syntax by Ben Ambridge. 6. Learning Syntactic Structures from String Input by Ethan Gotlieb Wilcox, Jon Gauthier, Jennifer Hu, Peng Qian, and Roger Levy. 7. Analyzing Discourse Knowledge in Pre-Trained LMs by Sharid Lo´aiciga. 8. Linguistically Guided Multilingual NLP by Olga Majewska, Ivan Vuli´c, and Anna Korhonen. 9. Word Embeddings are Word Story Embeddings (and that’s fine) by Katrin Erk and Gabriella Chronis. 10. Algebra and Language: Reasons for (Dis)content by Lawrence S. Moss. 11. Unitary Recurrent Networks by Jean-Philippe Bernardy and Shalom Lappin.

Shalom Lappin is a Professor of Computational Linguistics at the University of Gothenburg, Professor of Natural Language Processing at Queen Mary University of London and Emeritus Professor of Computational Linguistics at King’s College London. His research focuses on the application of machine learning and probabilistic models to the representation and the acquisition of linguistic knowledge.Jean-Philippe Bernardy is a researcher at the University of Gothenburg. His main research interest is in interpretable linguistic models, in particular, those built from first principles of algebra, probability and geometry.

“Lappin and Bernardy have assembled a great set of researchers who work on linguistic, cognitive science and natural language processing in deep neural network approaches to language. The result is a state of the art collection of interest to anyone with interests in DNNs and their connection to human language.” --Edward A. F. Gibson, Professor, MIT Department of Brain & Cognitive Sciences

• 1. On the Proper Role of Linguistically Oriented Deep Net Analysis in Linguistic Theorizing by Marco Baroni. 2. What Artificial Neural Networks Can Tell Us About Human Language Acquisition by Alex Warstadt and Samuel R. Bowman. 3. Grammar through Spontaneous Order by Nick Chater and Morten H. Christiansen. 4. Language is Acquired in Interaction by Eve V. Clark. 5. Why Algebraic Systems aren’t Sufficient for Syntax by Ben Ambridge. 6. Learning Syntactic Structures from String Input by Ethan Gotlieb Wilcox, Jon Gauthier, Jennifer Hu, Peng Qian, and Roger Levy. 7. Analyzing Discourse Knowledge in Pre-Trained LMs by Sharid Lo´aiciga. 8. Linguistically Guided Multilingual NLP by Olga Majewska, Ivan Vuli´c, and Anna Korhonen. 9. Word Embeddings are Word Story Embeddings (and that’s fine) by Katrin Erk and Gabriella Chronis. 10. Algebra and Language: Reasons for (Dis)content by Lawrence S. Moss. 11. Unitary Recurrent Networks by Jean-Philippe Bernardy and Shalom Lappin.