Deep nets trained on large amounts of unannotated text develop impressive linguistic skills. For years now, linguistically-inclined computational linguists have systematically studied the behaviour of these models through a variety of grammatical tasks, in search for new insights on the nature of language. However, this line of work has had virtually no impact on theoretical linguistics. In my talk, after reviewing some of the most exciting work in the area, I would like to provide some conjectures about why theoretical linguists do not care, and suggest a few possible avenues for a more fruitful convergence between the fields.
Marco Baroni received a PhD in Linguistics from the University of California, Los Angeles, in the year 2000. After several experiences in research and industry, he joined the Center for Mind/Brain Sciences of the University of Trento, where he became associate professor in 2013. In 2016, Marco joined the Facebook Artificial Intelligence Research team in Paris. In 2019, he became ICREA research professor, affiliated with the Linguistics Department of Pompeu Fabra University in Barcelona. Marco’s work in the areas of multimodal and compositional distributed semantics has received widespread recognition, including a Google Research Award, an ERC Starting Grant, the ICAI-JAIR best paper prize and the ACL test-of-time award. Marco’s current research focuses on how to improve communication between artificial neural networks, taking inspiration from human language and other animal communication systems.