From e28daa3f7a22dc5e2d92eb4b7a3d8d2a1b4c5df6 Mon Sep 17 00:00:00 2001
From: jaste110 <jan.steimann@hhu.de>
Date: Fri, 14 Aug 2020 08:53:58 +0200
Subject: [PATCH] Add baseline methods
---
slides/methods.tex | 31 +++++++++++++++++++++++++++++--
1 file changed, 29 insertions(+), 2 deletions(-)
diff --git a/slides/methods.tex b/slides/methods.tex
index 8294760..cbbcf37 100644
--- a/slides/methods.tex
+++ b/slides/methods.tex
@@ -1,5 +1,32 @@
\section{Methods}
-\subsection{Methods}
+\subsection{Baseline Methods}
\begin{frame}
- This is the third slide \cite{wachsmuth:2017a}.
+ \frametitle{Similarity}
+ \begin{itemize}
+ \item For the ranking of arguments, we measured the semantic similarity
+ between premise and conclusion
+ \item Here each word of the argument in embedded in a vector space and then the
+ average of the vectors of the argument is calculated
+ \item The similarity of a premise and a conclusion is the calculated by the
+ angle between them
+ \item In the course of this experiment, we used three different embeddings
+ \begin{itemize}
+ \item BERT\footnote{J. Devlin, M. Chang, K. Lee, and K. Toutanova, “BERT: pre-training of deep bidirectional transformers
+for language understanding,”}
+ \item Elmo\footnote{M. E. Peters, M. Neumann, M. Iyyer, M. Gardner, C. Clark, K. Lee, and L. Zettlemoyer, “Deep
+contextualized word representations,”}
+ \item Glove\footnote{J. Pennington, R. Socher, and C. Manning, “Glove: Global vectors for word representation,”}
+ \end{itemize}
+ \end{itemize}
+\end{frame}
+
+
+\begin{frame}
+ \frametitle{Sentiment}
+ \begin{itemize}
+ \item Another approach to rank the argument is to measure how positive the tone
+ of the premises is
+ \item For this, we use a sentiment neural network based on FastText\footnote{A. Joulin, E. Grave, P. Bojanowski, and T. Mikolov, “Bag of tricks for efficient text classification,”}, which was
+ trained on film ratings of IMDb
+ \end{itemize}
\end{frame}
\ No newline at end of file
--
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