14 Aug 2016
Bolzano, Italy
Composes end-of-project workshop: Call for participation Workshop website: http://clic.cimec.unitn.it/composes/workshop.html The end-of-project workshop of the Composes project (http://clic.cimec.unitn.it/composes/) will take place on Sunday August 14th 2016 in Bolzano (Italy), as a satellite event of ESSLLI 2016 (http://esslli2016.unibz.it/). The workshop will be an occasion to discuss some exciting topics in computational semantics, with some great invited speakers/panelists leading the discussion. We foresee a mixture of position statements on the topics below by the invitees and audience participation in the form of open debates. Speakers/Panelists: - Nicholas Asher - Marco Baroni - Stephen Clark - Emmanuel Dupoux - Katrin Erk - Adele Goldberg - Alessandro Lenci - Hinrich Schütze - Jason Weston Topics: - Lessons learned from the Composes project: Which problems were we trying to solve? Have we solved them? Have new-generation neural networks made compositional distributional semantics obsolete? - End-to-end models and linguistics: What is the role of linguistics in the (new) neural network/end-to-end/representation learning era? Do such systems need linguistics at all? Are some linguistic theories better tuned to them than others? Is there an appropriate vocabulary of linguistic units for end-to-end systems? Is compositionality a solved problem? Which linguistic challenges are difficult to tackle with neural networks? - "Fuzzy" vs "precise" (concepts vs entities, generics vs specifics, lexical vs phrasal/discourse semantics, analogy vs reasoning, sense vs reference): Are learning-based statistical methods only good at fuzzy? Can new-generation neural networks (Memory Networks, Stack RNNs, NTMs etc) handle both fuzzy and precise? Is fuzzy a solved problem? - Learning like humans do: If we want to develop systems reaching human-level language understanding, what is the appropriate input? What should training data and objective functions look like? What are appropriate tests of success? Assuming our methods are much more data-hungry than human learning is, why is this the case? Ideas for fixing that? What ways can we teach our models to understand, other than through expensive labeling of data? Please visit the workshop website for information about (free) registration and for updates: http://clic.cimec.unitn.it/composes/workshop.html -- [LOGIC] mailing list http://www.dvmlg.de/mailingliste.html Archive: http://www.illc.uva.nl/LogicList/ provided by a collaboration of the DVMLG, the Maths Departments in Bonn and Hamburg, and the ILLC at the Universiteit van Amsterdam