diff --git a/docs/final-report/report.pdf b/docs/final-report/report.pdf
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diff --git a/docs/final-report/report.tex b/docs/final-report/report.tex
index 229eac5ae9e6b23496667d3d9fd640a1c7acc6f0..ef6f8cc404c5d6299186e7bd319edb9955b503d4 100644
--- a/docs/final-report/report.tex
+++ b/docs/final-report/report.tex
@@ -13,12 +13,16 @@
 
 \begin{document}
 
+\pagenumbering{roman}
+
 \maketitle
 
 \tableofcontents
 
 \pagebreak
 
+\pagenumbering{arabic}
+
 \section{Project Description}
 
     \subsection{Conversational AI and Training}
@@ -26,15 +30,15 @@
     conversation. One important goal is to make the conversation seem as natural as possible.
     Ideally, an interaction with the bot should be indistinguishable from one with a human. This
     can make communication with a computer become very pleasant and easy for humans as
-    they are simply using their natural language.
-    \\ Conversational AI can be used in Voice Assistants that communicate through spoken words or
+    they are simply using their natural language. \par
+    Conversational AI can be used in Voice Assistants that communicate through spoken words or
     through chatbots that imitate a human by sending text messages.
 
     \subsection{Rasa Framework}
     Rasa is a collection of tools for conversational AI software. The \textit{Rasa Stack} consists
     of two open source libraries called \textit{Rasa NLU} and \textit{Rasa Core} that can be used to create contextual
-    chatbots.
-    \\ A Rasa Bot needs training data to work properly.
+    chatbots. \par
+    A Rasa Bot needs training data to work properly.
 
     \subsection{Research Question}
     The objective of this project is to find out, whether chatbots can be trained with natural
@@ -95,7 +99,7 @@
     it is fragmented into somewhat arbitrary subcategories and thus not optimal to use as a
     collection.
     However Wikipedia also has a \textit{List of physicists}\footnote{\url{https://en.wikipedia.org/wiki/List_of_physicists}} which contains 981 physicists and was
-    used to build the collection used. \\
+    used to build the collection used. \par
     Data scraping was done using the R Package \textit{WikipediR}, a wrapper around the Wikipedia
     API.
     Articles were downloaded as HTML\footnote{HTML was chosen over wikitext to ease text cleaning}
@@ -106,7 +110,7 @@
     As all approaches leverage on some form of NER or POS tagging, annotations were created for all
     texts.
     This was done using the R Package \textit{cleanNLP} with a spaCy backend to create NER and POS
-    tags, as well as lemmatization. \\
+    tags, as well as lemmatization. \par
     Fact extraction for physicists spouses was done using pre-defined patterns on word
     lemmata.\footnote{Functionality to use patterns on POS tags is also available but did not yield
     a better outcome.}
@@ -114,7 +118,7 @@
     places to look for the name of the physicist and his/her spouse.
     When a matching phrase is found the results are verified by checking that the correct
     physicist is mentioned as well as the potential spouse being detected as a person by the NER
-    tagger.
+    tagger. \par
     A different approach is used for the get\_awards() function. The approach is based on the assumption that the NER tagger will tag the awards as some kind of entity. A set of keywords is
     the used to extract all entities of interest, the awards.
 
@@ -124,7 +128,7 @@
     and \textit{Rasa NLU}. The \textit{Rasa NLU} component takes care of getting user input and
     matching it with the respective intents. The \textit{Rasa-Core} component executes all actions
     associated with the determined intent. Configuration has been organized in reference to
-    examples from the Rasa github repository\footnote{\url{https://github.com/RasaHQ/rasa_core/tree/master/examples}}. \\
+    examples from the Rasa github repository\footnote{\url{https://github.com/RasaHQ/rasa_core/tree/master/examples}}. \par
     Rasa NLU has been trained with example questions in markdown format that contain highlighted
     entities. This ensures that the bot is able to understand intents and extract the entities
     inside the sentences. One example can be seen in \ref{nlu_example}.