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-rw-r--r--report/intro_slides/final_slides.pdfbin1926728 -> 1826408 bytes
-rw-r--r--report/intro_slides/final_slides.tex118
2 files changed, 34 insertions, 84 deletions
diff --git a/report/intro_slides/final_slides.pdf b/report/intro_slides/final_slides.pdf
index e43a6d82..8d67cd20 100644
--- a/report/intro_slides/final_slides.pdf
+++ b/report/intro_slides/final_slides.pdf
Binary files differ
diff --git a/report/intro_slides/final_slides.tex b/report/intro_slides/final_slides.tex
index f552a8b4..b37348b5 100644
--- a/report/intro_slides/final_slides.tex
+++ b/report/intro_slides/final_slides.tex
@@ -299,7 +299,7 @@
\end{exampleblock}
\begin{itemize}
\only<1>{\item S -> $\langle$ NP\ind{1} VP\ind{2}, NP\ind{1} VP\ind{2} $\rangle$, \\ NP -> $\langle$ PRP\ind{1}, PRP\ind{1} $\rangle$}
-\only<1>{\item PRP -> $\langle$ Je, I $\rangle$, \\ VP -> $\langle$ ne veux pas VB\ind{1}, do not want to VB\ind{1} $\rangle$, \\ VB -> $\langle$ travailler, work $\rangle$}
+\only<1>{\item PRP -> $\langle$ Je, I $\rangle$, VB -> $\langle$ travailler, work $\rangle$ \\ VP -> $\langle$ ne veux pas VB\ind{1}, do not want to VB\ind{1} $\rangle$}
\only<2->{
\item Strong model of sentence structure.
\item Reliant on a treebank to train the parser.
@@ -471,13 +471,23 @@ Output:
\begin{frame}[t]{Workshop Streams}
-Expand, describing challenges faced in each stream.
-\vspace{0.25in}
-\begin{unpacked_itemize}
-\item Implement scalable SCFG grammar extraction algorithms.
-\item Improve SCFG decoders to efficiently handle the grammars produce.
-\item Investigate discriminative training regimes to leverage features extracted from these grammars.
-\end{unpacked_itemize}
+\begin{enumerate}
+\item Implement scalable labelled SCFG grammar induction algorithms:
+\begin{itemize}
+\item by clustering translation phrases which occur in the same context we can learn which phrases are substituteable,
+\item we have implemented both parametric and non-parametric Bayesian clustering algorithms.
+\end{itemize}
+\item Improve SCFG decoders to efficiently handle the grammars produced:
+\begin{itemize}
+\item translation complexity scales quadratically as we add more categories,
+\item in order to decode efficiently with the grammars we've induced we have created faster search algorithms tuned for syntactic grammars.
+\end{itemize}
+\item Investigate discriminative training regimes to leverage features extracted from these grammars:
+\begin{itemize}
+\item to make the most of our induced grammars we need discriminative training algorithms that learn from more than a handful of features,
+\item we've implemented two large scale discriminative algorithms for training our models.
+\end{itemize}
+\end{enumerate}
\end{frame}
\begin{frame}[t]{Extrinsic evaluation: Bleu}
@@ -565,7 +575,7 @@ Expand, describing challenges faced in each stream.
\item Europarl Dutch-French:
\begin{itemize}
\item 100k sentence pairs, standard Europarl test sets
- \item Hiero baseline score: Europarl 2008 - 26.3 (1 reference)
+ \item Hiero baseline score: Europarl 2008 - 15.75 (1 reference)
\item Major challenges: V2 / V-final word order, morphology
\end{itemize}
\end{unpacked_itemize}
@@ -595,11 +605,11 @@ Expand, describing challenges faced in each stream.
\end{figure}
\end{exampleblock}
- \vspace{0.25in}
+ %\vspace{0.25in}
\end{column}
\begin{column}{0.7\textwidth}
\begin{unpacked_itemize}
- \item 1:55pm Experimental Setup. Trevor
+ \item 1:55pm Grammar induction and evaluation. Trevor
\item 2:10pm Non-parametric models of category induction. Chris
\item 2:25pm Inducing categories for morphology. Jan
\item 2:35pm Smoothing, backoff and hierarchical grammars. Olivia
@@ -635,7 +645,6 @@ Expand, describing challenges faced in each stream.
\end{figure}
\end{exampleblock}
- \vspace{0.25in}
\end{column}
\begin{column}{0.7\textwidth}
\begin{itemize}
@@ -661,8 +670,6 @@ Expand, describing challenges faced in each stream.
\end{unpacked_itemize}
\end{frame}
-\begin{frame}[t]{This slide is intentionally left blank.}
-\end{frame}
\begin{frame}[t]{Outline}
\begin{columns}
@@ -687,12 +694,12 @@ Expand, describing challenges faced in each stream.
\end{figure}
\end{exampleblock}
- \vspace{0.25in}
+ %\vspace{0.25in}
\end{column}
\begin{column}{0.7\textwidth}
\begin{unpacked_itemize}
- \only<1>{\item \alert{1:55pm Motivation and experimental methodology. Trevor}}
- \only<2->{\item 1:55pm Motivation and experimental methodology. Trevor}
+ \only<1>{\item \alert{1:55pm Grammar induction and evaluation. Trevor}}
+ \only<2->{\item 1:55pm Grammar induction and evaluation. Trevor}
\only<2>{\item \alert{2:10pm Non-parametric models of category induction. Chris}}
\only<1,3->{\item 2:10pm Non-parametric models of category induction. Chris}
\only<3>{\item \alert{2:25pm Inducing categories for morphology. Jan}}
@@ -733,83 +740,26 @@ Expand, describing challenges faced in each stream.
\end{figure}
\end{exampleblock}
- \vspace{0.25in}
\end{column}
\begin{column}{0.7\textwidth}
\begin{itemize}
\setlength{\itemsep}{25pt}
\setlength{\parskip}{0pt}
\setlength{\parsep}{0pt}
- \only<1>{\item \alert{3:15pm Training models with rich features spaces. Vlad}}
- \only<2->{\item 3:15pm Training models with rich features spaces. Vlad}
- \only<2>{\item \alert{3:30pm Decoding with complex grammars. Adam}}
- \only<1,3->{\item 3:30pm Decoding with complex grammars. Adam}
- \only<3>{\item \alert{4:00pm Closing remarks. Phil}}
- \only<1,2,4->{\item 4:00pm Closing remarks. Phil}
- \only<4>{\item \alert{4:05pm Finish.}}
- \only<1-3>{\item 4:05pm Finish.}
+ \only<1>{\item \alert{3:20pm Parametric models: posterior regularisation. Desai}}
+ \only<2->{\item 3:20pm Parametric models: posterior regularisation. Desai}
+ \only<2>{\item \alert{3:35pm Training models with rich features spaces. Vlad}}
+ \only<1,3->{\item 3:35pm Training models with rich features spaces. Vlad}
+ \only<3>{\item \alert{3:50pm Decoding with complex grammars. Adam}}
+ \only<1,2,4->{\item 3:50pm Decoding with complex grammars. Adam}
+ \only<4>{\item \alert{4:20pm Closing remarks. Phil}}
+ \only<1-3,5->{\item 4:20pm Closing remarks. Phil}
+ \only<5>{\item \alert{4:25pm Finish.}}
+ \only<1-4>{\item 4:25pm Finish.}
\end{itemize}
\end{column}
\end{columns}
\end{frame}
-\begin{frame}[t]{This slide is intentionally left blank.}
-\end{frame}
-
-
-\begin{frame}[t]{Statistical machine translation: state-of-the-art}
-%\vspace{1.0cm}
-\begin{exampleblock}{Urdu $\rightarrow$ English}
- \begin{figure}
- {\centering \includegraphics[scale=0.55]{urdu-bl.pdf}}
- \end{figure}
-\end{exampleblock}
-\begin{itemize}
- \item Current state-of-the-art translation models struggle with language pairs which exhibit large differences in structure.
-\end{itemize}
-\end{frame}
-
-\begin{frame}[t]{Statistical machine translation: our unsupervised grammars}
-%\vspace{1.0cm}
-\begin{exampleblock}{Urdu $\rightarrow$ English}
- \begin{figure}
- {\centering \includegraphics[scale=0.55]{urdu-25hp.pdf}}
- \end{figure}
-\end{exampleblock}
-\begin{itemize}
- \item In this workshop we've made some small steps towards better translations for difficult language pairs.
-\end{itemize}
-\only<2->{
- Google Translate: \\
- \hspace{0.25in} {\em *After the attack a number of local residents has blank areas.}
-}
-\end{frame}
-
-
-\begin{frame}[t]{Induced Translation Structure}
-\begin{center}
-\includegraphics[scale=0.32]{joshua_tree19.pdf}
-\end{center}
-\end{frame}
-
-\begin{frame}[t]{What we've achieved:}
- \vspace{0.5in}
- \begin{unpacked_itemize}
- \item
- \item
- \end{unpacked_itemize}
-\end{frame}
-
-
-\begin{frame}[t]{We're we'll go from here:}
- \vspace{0.5in}
- \begin{unpacked_itemize}
- \item
- \item
- \end{unpacked_itemize}
-\end{frame}
-
-
-
\end{document}
generated by cgit v1.2.3 (git 2.39.1) at 2024-11-15 15:57:11 +0000