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% title info
\title{AI-Enhanced High-Accuracy Robotics for Industrial Applications}
\subtitle{Application for PhD-Position (m/f/d) in Industrial Robotics}
\author{\href{https://abanbytes.eu}{David Madl}}
%\institute{\href{https://www.biostat.wisc.edu}{Biostatistics \& Medical Informatics} \\[2pt] \href{http://www.wisc.edu}{University of Wisconsin{\textendash}Madison}}
\date{%\href{http://kbroman.org}{\tt \scriptsize kbroman.org}
%\\[-4pt]
\href{https://github.com/cidermole}{\tt \scriptsize github.com/cidermole}
}


\begin{document}

% title slide
{
\setbeamertemplate{footline}{} % no page number here
\frame{
  \titlepage
  \note{
} } }

\begin{frame}{Me - personal interests}

\vspace{24pt}

\bi
%\item{\emojifont🏐 Volleyball}
%\item{\emojifont🧗 Bouldering}
%\item{\emojifont💃 Tango Argentino}
%\item{\emojifont♘ Chess}
%\item{\emojifont🌎 South America}

\item{\inlineemoji{Images/volley.png} Volleyball}
\item{\inlineemoji{Images/boulder.png} Bouldering}
\item{\inlineemoji{Images/tango.png} Tango Argentino}
\item{\inlineemoji{Images/chess.png} Chess}
\item{\inlineemoji{Images/sa.png} South America}
\ei

\end{frame}


\begin{frame}{MSc Thesis}
\subt{Handling out-of-vocabulary words in a domain adaptation setting in SMT}

%\vspace{12pt}

\begin{itemize}
  \item{Phrase-based Statistical Machine Translation\vspace{0.5em}}
  \begin{itemize}
    \addtolength{\itemsep}{0.7em}
    \item{Warren Weaver (1947):\\[0.5em]
    \textit{``This [article in Russian] is really written in English, but it has been coded in some strange symbols. I will now proceed to decode.''}}
    \item{Bayes Theorem \& independence assumption:\\[0.5em]
    $P(\text{en}|\text{ru}) = \frac{P(\text{ru}|\text{en}) P(\text{en})}{P(\text{ru})}$\\[0.5em]
    $P(\text{ru}|\text{en}) = \prod_{i}^{M} P(\text{phrase\_ru}_{i}|\text{en})$ \hspace{0.5em} translation model\\[0.5em]
    $P(\text{en}) = \prod_{k}^{L} P(w_{k}|w_{k-n}...w_{k-1})$ \hspace{0.5em} language model\\[0.5em]
    $P(\text{ru})$ \hspace{0.5em} dropped normalization factor
    }
  \end{itemize}
\end{itemize}

{\tiny see e.g. (Koehn et al 2003, ``Statistical phrase-based translation'')}

\note{
  The rules for the translation model are more complex than shown here, because of the possibility of phrase splits at different word boundaries.

  The probabilities on the \textbf{right-hand side} are estimated from a training corpus. 
  The language model is estimated as transitions of an n-state \textbf{Hidden Markov Model}. 
  The translation model obtains phrases from a previous optimization called \textbf{Word Alignment}.
}

% {\color{hilight} b}

\end{frame}


\begin{frame}{MSc Thesis - Domain Adaptation 1}
\subtnc{Handling out-of-vocabulary words in a domain adaptation setting in SMT}

\vspace{12pt}

\bi
  \item{{\color{hilight} test} and {\color{vhilight} train} datasets}\\[0.5em]
  \item{{\color{hilight} medical} and {\color{vhilight} political} domains}\\[0.5em]
  \item{{\color{hilight} 5 M} and {\color{vhilight} 50 M} word tokens}\\[0.5em]
  \item{Domain adaptation:\\[0.5em]
    ${\color{hilight} P(\text{en}|\text{ru})} = \frac{\color{vhilight} P(\text{ru}|\text{en}) P(\text{en})}{P(\text{ru})}$\\[0.5em]
    ${\color{vhilight} P(\text{ru}|\text{en}) = \prod_{i}^{M} P(\text{phrase\_ru}_{i}|\text{en})}$ \hspace{0.5em} translation model\\[0.5em]
    ${\color{vhilight} P(\text{en}) = \prod_{k}^{L} P(w_{k}|w_{k-n}...w_{k-1})}$ \hspace{0.5em} language model\\[0.5em]
  }
\ei

\note{
  In domain adaptation, we have a distributional mismatch between training and test data. 
  Simply appending target domain text to a large training dataset is not optimal. 
  This is because the statistics of the original domain dominate.
}

\end{frame}


\begin{frame}{MSc Thesis - Domain Adaptation 2}
\subtnc{Handling out-of-vocabulary words in a domain adaptation setting in SMT}

\vspace{12pt}

\bi
  \item{{\color{hilight} medical} and {\color{vhilight} political} domains}\\[0.5em]
  \item{Mixture model:\\[0.5em]
    $P(\text{ru}|\text{en}) = \alpha_1 {\color{hilight} P_1(\text{ru}|\text{en})} + \alpha_2 {\color{vhilight} P_2(\text{ru}|\text{en})} $\\[0.5em]
    $P(\text{en}) = \alpha_1 {\color{hilight} P_1(\text{en})} + \alpha_2 {\color{vhilight} P_2(\text{en})} $\\[0.5em]
  }
  \item{Optimize quality measure:\\[0.5em]
    $argmax_{\symbf{\alpha}} \text{BLEU}(\symbf{\alpha})$, $\sum_i \alpha_i = 1$}
\ei

\note{
  We can do better by estimating two models, one on each domain. 
  Then we optimize the resulting translation quality based on the mixture parameters.
}

\end{frame}


\begin{frame}{MSc Thesis - Word Alignment oracle}
\subt{Handling out-of-vocabulary words in a domain adaptation setting in SMT}

\begin{center}
\ig[width=0.6\textwidth]{Images/oov.png}
\end{center}

{\tiny source: Figure 6.1.3b, MSc Thesis}

\note{
  The thesis topic I was assigned was to investigate words which could not be translated. 
  The oracle experiments are the most insightful. This one shows, for different training set sizes:
  * in green, the **theoretical limit** from training data,
  * in red, if **Word Alignment** had full statistics,
  * in blue, the actual errors.
}

\end{frame}


\end{document}