Extend introduction text

paper/10_introduction.tex

@@ -21,4 +21,15 @@ Classification of Disease version 10 (ICD-10). The task has been carried out the
 last two years of the lab, however was only concerned with French and English
 certificates. In contrast, the organizers provided annotated death reports as
 well as ICD-10 dictionaries for French, Italian and Hungarian this year. The
-development of language-independent, multilingual approaches was encouraged.
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+development of language-independent, multilingual approaches was encouraged.
+
+Inspired by the recent success of recurrent neural network models
+\cite{cho_learning_2014,lample_neural_2016,dyer_transition-based_2015} in
+general and the convincing performance of the work from Miftahutdinov and
+Tutbalina \cite{miftakhutdinov_kfu_2017} in the last year's competition we opt
+for the development of a deep learning model for this year's task. Our work
+introduces a language independent approach for ICD-10 classification using
+multi-language word embeddings and LSTM-based recurrent models. We divide the
+the classification into two tasks. First, we extract symptoms from a certificate
+line backed by an encoder-decoder model. Given the symptoms the actual ICD-10
+classification will be performed by a separate LSTM model.
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