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BEGIN:VEVENT
UID:/NewsandEvents/Archives/2017/newsitem/8687/23-
 March-2017-Computational-Linguistics-Seminar-Raffa
 ella-Bernardi
DTSTAMP:20170320T141501
SUMMARY:Computational Linguistics Seminar, Raffael
 la Bernardi
ATTENDEE;ROLE=Speaker:Raffaella Bernardi (Trento)
DTSTART;TZID=Europe/Amsterdam:20170323T170000
LOCATION:Room F3.20, Science Park 107, Amsterdam
DESCRIPTION:Linguistics quantifiers have been the 
 realm of Formal Semantics. A lot is known about th
 eir formal properties and how those properties aff
 ect logical entailment, the licensing of polarity 
 item, or scope ambiguities. Less is known about ho
 w quantifiers are acquired by children and even le
 ss about how computational models can learn to qua
 ntify objects in images. In this talk, we will rep
 ort on our findings in this direction. First of al
 l, we will explain why the task is interesting and
  challenging for a Language and Vision model. Seco
 ndly, we will report our evaluation of state-of-th
 e-art neural network models against this task. Thi
 rdly, we will compare the acquisition of quantifie
 rs with the acquisition of cardinals. We will show
  that a model capitalizing on a `fuzzy' measure of
  similarity is effective for learning quantifiers,
  whereas the learning of exact cardinals is better
  accomplished when information about number is pro
 vided.
X-ALT-DESC;FMTTYPE=text/html:\n  <p>Linguistics qu
 antifiers have been the realm of Formal Semantics.
  A lot is known about their formal properties and 
 how those properties affect logical entailment, th
 e licensing of polarity item, or scope ambiguities
 . Less is known about how quantifiers are acquired
  by children and even less about how computational
  models can learn to quantify objects in images. I
 n this talk, we will report on our findings in thi
 s direction. First of all, we will explain why the
  task is interesting and challenging for a Languag
 e and Vision model. Secondly, we will report our e
 valuation of state-of-the-art neural network model
 s against this task. Thirdly, we will compare the 
 acquisition of quantifiers with the acquisition of
  cardinals. We will show that a model capitalizing
  on a `fuzzy' measure of similarity is effective f
 or learning quantifiers, whereas the learning of e
 xact cardinals is better accomplished when informa
 tion about number is provided.</p>\n
URL:https://www.illc.uva.nl/LaCo/CLS/
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