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Emmanuel Dupoux, September 5

Reverse Engineering Infant Language Acquisition using Machine Learning


The mechanisms that enable infants to spontaneously acquire their native(s) language(s) are still not well understood. I examine the conditions under which recent progress in machine learning can provide insights on these mechanisms. I first argue that computational models of language acquisition must address the full complexity of language learning, ie. address the variability and ambiguity of raw sensory signals, instead of dealing with artificial or simplified language inputs. I then show that although deep neural networks can overcome the variability of raw sensory signals, they rely on implausible supervised learning mechanisms. I  summarize recent work in the unsupervised discovery of linguistic units from continuous speech (phonemes and words), and the use of these algorithms to test hypotheses about the functional role of infant-directed speech.


 Date:                     Tuesday, September 5, 2017

Time:                     15h45

Location:              MPI Conference Room 163

                               Wundtlaan 1

                               6525 XD Nijmegen


Last checked 2017-09-04 by edith sjoerdsma

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