diff --git a/docs/final-report/report.tex b/docs/final-report/report.tex index 2b5c543adc6f5194d3f2b1eb9f46c7b57301d685..5a7150495c3fe3e8704688941626a5182b1c9098 100644 --- a/docs/final-report/report.tex +++ b/docs/final-report/report.tex @@ -2,7 +2,6 @@ \usepackage[utf8]{inputenc} \usepackage[english]{babel} \usepackage[T1]{fontenc} -\usepackage{ngerman} \usepackage[]{listings} \usepackage{hyperref} \usepackage{graphicx} @@ -56,7 +55,16 @@ \subsection{Rasa Setup and Intents} - The Rasa-Stack consists of two components: \textit{Rasa-Core} and \textit{Rasa-NLU}. The \textit{Rasa-NLU} component takes care of getting user input and matching it with the respective intents. It also extracts all possibly provided entities and stores them in variables, called ``slots''. After that, the \textit{Rasa-Core} component executes all actions associated with the determined intent. + The Rasa-Stack consists of two components: \textit{Rasa-Core} and \textit{Rasa-NLU}. + The \textit{Rasa-NLU} component takes care of getting user input and matching it with the respective intents. + It also extracts all possibly provided entities and stores them in variables, called ``slots''. + After that, the \textit{Rasa-Core} component executes all actions associated with the determined intent. + Every intent that requests data, uses two custom python actions: + One to search for the information in a datafile or database and a second one to utter found results back to the user. + There are fallback actions and error handlings in place, in case the user entered false requests or the bot can't find answers for a given intent. + The structure of the bot was heavily inspired by the Rasa Github examples\footnote{\url{https://github.com/RasaHQ/rasa_core/tree/master/examples}}. + Intents used in this project were chosen to be simple and possible to extract from wikitexts. + \subsection{Scrapping of Source Texts} Wikipedia was chosen as resource for texts as it provides texts of relatively long length in a somewhat uniform manner.