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Grzegorz Chrupała  committed 94c4466

More stuff in slides.

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File doc/clin-2012/slides.tex

 
 \DeclareSymbolFont{AMSb}{U}{msb}{m}{n}
 \DeclareMathOperator*{\argmax}{argmax}
-\newcommand{\tick}{\color{blue}\ding{51}}
-\newcommand{\cross}{\color{red}\ding{55}}
+\newcommand{\tick}{{\color{blue}\ding{51}}}
+\newcommand{\cross}{{\color{red}\ding{55}}}
 
 
 \mode<presentation>{ 
       Soft classes    & \tick     & \tick \\\hline
       Bayesian        & \cross    & \tick \\
       \bf Online      & \tick     & \cross \\
-      Unbounded       & \tick     & \cross \\
       \bf Parameters  & \cross    & \tick \\ 
+      Adaptive K      & \tick     & \cross \\
       Fast            & \cross    & \tick \\
     \end{tabular}
   \end{center}
   \item Incrementally optimizes a joint entropy criterion:
     \begin{small}
       \begin{equation*}
-        H(X,Y) =  {\color{red} H(Y)} + {\color{blue} H(X|Y)} 
+        H(X,Y) =  {\color{blue} H(X|Y)} + {\color{red} H(Y)} 
       \end{equation*}
     \end{small}
     \begin{itemize}
   \end{center}
 \end{frame}
 
+
+\begin{frame}
+   \frametitle{Word class LDA}
+   \begin{itemize}
+   \item Number of classes K is specified as a parameter
+   \item $\alpha$ and $\beta$ control sparsity of priors
+   \item Inference using Gibbs sampler (batch)
+   \end{itemize}
+\end{frame}
+
+
+\begin{frame}
+  \frametitle{Model evaluation}
+  \begin{center}
+    \begin{block}{}
+      Evaluate \vskip 0.5cm
+      \begin{itemize}
+      \item {\bf Parameterized} $\Delta$H
+      \item {\bf Online} Gibbs sampler for word class LDA
+      \end{itemize}\vskip 0.5cm
+      on the {\bf same task} and the {\bf same dataset}.
+    \end{block}
+  \end{center}
+\end{frame}
+
+\begin{frame}
+  \frametitle{Dataset}
+  \begin{itemize}
+  \item Manchester portion of CHILDES (mothers)
+  \item Discard one-word sentences and punctuation
+  \end{itemize}
+  \begin{center}
+    \begin{tabular}[!t]{l r r r }
+      \hline
+      {\bf Data Set} & {\bf Sessions} & {\bf \#Sent} & {\bf \#Words} \\
+      \hline
+      Training    & 26--28  & 22,491   & 125,339 \\ 
+      Development & 29--30  &  15,193     &  85,361 \\
+      \hline
+    \end{tabular}
+  \end{center}
+\end{frame}
+
+
+\begin{frame}
+  \frametitle{Task: word prediction}
+  \begin{itemize}
+  \item Relevant for cognitive modeling
+  \item Used in NLP -- language model evaluation 
+  \end{itemize}
+\end{frame}
+
+\begin{frame}
+  \frametitle{Word prediction}
+  \begin{itemize}\small
+  \item (Soft)-assign classes from context
+  \item Rank words based on predicted class
+  \end{itemize}
+\begin{block}{Reciprocal rank}
+  \small
+  \begin{tabular}{cc|c|cc}
+    want & to  & \color{gray} put & them & on   \\
+    \pause 
+    &     & $y_{123}$                 &      &   \\
+  \end{tabular}
+  \begin{tabular}{l|l|r}
+    $y_{123}$ 
+    &      make & \\
+    &      take & \\
+    & \color{red}put& $\textit{rank}^{-1}=\frac{1}{3}$\\
+    &      get & \\
+    &      sit & \\
+    &      eat & \\
+    &      let & \\
+  \end{tabular}
+\end{block}
+\end{frame}
+
+\begin{frame}
+   \frametitle{Parametrizing $\Delta$H}
+   \begin{itemize}
+   \item No free parameters in $\Delta$H
+     \begin{itemize}
+     \item[\tick] No need to optimize them separately
+     \item[\cross] Lack of flexibility
+     \end{itemize}
+   \item If we force parameterization
+     \begin{itemize}
+     \item Is the algorithm well-behaved?
+     \item Can we smoothly control the tradeoff?
+     \end{itemize}
+   \end{itemize}
+\end{frame}
+
+\begin{frame}
+  \begin{block}{Parametrized $\Delta$H}
+    \begin{small}
+      \begin{equation*}
+        H_{\alpha}(X,Y) =  {\color{blue} \alpha H(X|Y)} + {\color{red} (1-\alpha) H(Y)} 
+      \end{equation*}
+    \end{small}
+  \end{block}
+ \end{frame}
+
+
+
+
+
+\begin{frame}
+  \begin{center}
+    \large Thank you
+  \end{center}
+\end{frame}
+
+\begin{frame}
+   \frametitle{Word prediction: variants}
+   \begin{itemize}
+   \item $\Delta H_{\max}$
+     \[
+     P(w|h) = P(w|\argmax_i R(y_i|h)^{-1})
+     \]
+   \item $\Delta H_\Sigma$
+     \[
+       P(w | h) = \sum_{i=1}^N P(w | y_i) \frac{\mathrm{R}(y_i|h)^{-1}}{\sum_{i=1}^N \mathrm{R}(y_i | h)^{-1}}
+     \]
+   \end{itemize}
+ \end{frame}
 \end{document}