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04-00-use-cases-for-nlp.Rmd
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04-00-use-cases-for-nlp.Rmd
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# Use-Cases for NLP
*Author: Matthias Aßenmacher*
Since it is all fine and good to know the theory and how everything's working on the inside,
we thought that this booklet could also benefit from showcasing some exemplary use case(s).
Initially we had planned to include two chapters on different use cases, but during the course
a student assigned to one of these chapters dropped out of the course. So we were left with only
one (but highly motivated) student willing to pursue this issue.
The following chapter will contain a use case on Natural Language Generation (NLG) which is a task
that (oftentimes) strongly relies on an encoder-decoder style architecture. Recently, these types
of models could benefit __a lot__ from the use of Attention mechanisms (@bahdanau2014neural) and the
proposal of the Transformer architecture (@vaswani2017attention) presented in Chapter 8.
Nevertheless the task of NLU requires more than just good architectures, it is also necessary to
handle the output of these models in a suitable way in order to generate meaningful text.
So without further ado: The following chapter will showcase the challenges of NLU by touching upon
the the tow topic _Chatbots_ and _Image Captioning_, where the latter represents a hybrid task of
Computer Vision and NLP.