=Paper=
{{Paper
|id=Vol-2852/keynote1
|storemode=property
|title=Learning about Language from Data - Abstract
|pdfUrl=https://ceur-ws.org/Vol-2852/abstract3.pdf
|volume=Vol-2852
|authors=David Talbot
}}
==Learning about Language from Data - Abstract==
<pdf width="1500px">https://ceur-ws.org/Vol-2852/abstract3.pdf</pdf>
<pre>
LEARNING ABOUT LANGUAGE FROM DATA - ABSTRACT
David Talbot

    Yandex Translate

                 Abstract
                 Modern neural network-based methods, known as “deep learning”, have transformed natural
                 language processing (NLP) over the past 10 years with unprecedented progress on tasks such
                 machine translation, question answering, dialogue systems and text generation. This isn’t the
                 first time that statistical learning has taken the field of NLP hostage, leaving apparently little
                 room for linguistics, but somehow this time it feels different. In this talk, I will summarize how
                 the field of NLP has changed over the past 10 years under the influence of deep learning, how
                 this is similar to previous waves of empiricism and how it differs, which problems have been
                 solved, which remain illusive and why deep learning, while ostensibly pushing linguistics out
                 of picture, may in fact be opening up new research directions for linguists.




Proceedings of the Linguistic Forum 2020: Language and Artificial Intelligence, November 12-14, 2020, Moscow, Russia
EMAIL: talbot@yandex-team.ru
ORCID: 0000-0002-5928-017X
              ©️ 2020 Copyright for this paper by its authors.
              Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
              CEUR Workshop Proceedings (CEUR-WS.org)

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