Tutorials

Linguistic Typology and Computational Language Learning, Cross-Lingual NLP
Ivan Vulic
University of Cambridge, UK
Abstract: Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.

Deep Learning, NLG and Dialogue
Konstas Ioannis
Heriot Watt University, UK
Abstract: Abstract: Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.

Analysis of representations emerging in multilayer recurrent neural networks
Grzegorz Chrupała
Tilburg University, Netherlands
Abstract: Abstract: Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.

Introduction to Distributed Representations in NLP
Fabio Massimo Zanzotto
Univ. Roma, Tor Vergata
Possible Structure: Distributed models of word meaning (count-based and language modelling), text and sense embeddings, BERT-like representations, Models of compositionality in DSM (addition, multiplication etc.). Data-driven metrics for Similarity.